diff --git a/11_deep_learning.ipynb b/11_deep_learning.ipynb
index 2c94b59..c002217 100644
--- a/11_deep_learning.ipynb
+++ b/11_deep_learning.ipynb
@@ -92,25 +92,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure sigmoid_saturation_plot\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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gY/EJIbL18oaX2fH3Dj5/egkb5ndn8WJj+7//bUwwaFPEvuL6+voSFxfHl19+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\nkv4fpnIt6Q3iZsAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fda10085630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "z = np.linspace(-5, 5, 200)\n",
     "\n",
@@ -219,25 +201,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure leaky_relu_plot\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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oMjjnOPPMM1m7di3z5s3j448/plu3bpx88sl8//33AOTl5dG5c2fmzZvH559/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\nAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd9db0cf518>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(z, leaky_relu(z, 0.05), \"b-\", linewidth=2)\n",
     "plt.plot([-5, 5], [0, 0], 'k-')\n",
@@ -399,18 +363,7 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
-      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
-      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
-      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from tensorflow.examples.tutorials.mnist import input_data\n",
     "mnist = input_data.read_data_sets(\"/tmp/data/\")"
@@ -422,22 +375,7 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Batch accuracy: 0.86 Validation accuracy: 0.9044\n",
-      "5 Batch accuracy: 0.94 Validation accuracy: 0.9508\n",
-      "10 Batch accuracy: 0.96 Validation accuracy: 0.9666\n",
-      "15 Batch accuracy: 1.0 Validation accuracy: 0.9722\n",
-      "20 Batch accuracy: 1.0 Validation accuracy: 0.975\n",
-      "25 Batch accuracy: 1.0 Validation accuracy: 0.9766\n",
-      "30 Batch accuracy: 0.98 Validation accuracy: 0.9782\n",
-      "35 Batch accuracy: 0.96 Validation accuracy: 0.9792\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 40\n",
     "batch_size = 50\n",
@@ -479,25 +417,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure elu_plot\n"
-     ]
-    },
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xAbT//jocJyLhCmoPajnQHzgfaFSdNxYWwvz5/vBT6Vdxcfnjq2qrqn3Hjj2v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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd9db0ae7f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(z, elu(z), \"b-\", linewidth=2)\n",
     "plt.plot([-5, 5], [0, 0], 'k-')\n",
@@ -574,25 +494,7 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure selu_plot\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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WwvPPw6xZMHeub3wAf8v1IUP8VSDy80MtUURCpoCSZuMcLFsG998PDz4Ia9f6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\nB9c9MCs0b4SXAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd9d5f2b940>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(z, selu(z), \"b-\", linewidth=2)\n",
     "plt.plot([-5, 5], [0, 0], 'k-')\n",
@@ -617,24 +519,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Layer 0: -0.26 < mean < 0.27, 0.74 < std deviation < 1.27\n",
-      "Layer 10: -0.24 < mean < 0.27, 0.74 < std deviation < 1.27\n",
-      "Layer 20: -0.17 < mean < 0.18, 0.74 < std deviation < 1.24\n",
-      "Layer 30: -0.27 < mean < 0.24, 0.78 < std deviation < 1.20\n",
-      "Layer 40: -0.38 < mean < 0.39, 0.74 < std deviation < 1.25\n",
-      "Layer 50: -0.27 < mean < 0.31, 0.73 < std deviation < 1.27\n",
-      "Layer 60: -0.26 < mean < 0.43, 0.74 < std deviation < 1.35\n",
-      "Layer 70: -0.19 < mean < 0.21, 0.75 < std deviation < 1.21\n",
-      "Layer 80: -0.18 < mean < 0.16, 0.72 < std deviation < 1.19\n",
-      "Layer 90: -0.19 < mean < 0.16, 0.75 < std deviation < 1.20\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "np.random.seed(42)\n",
     "Z = np.random.normal(size=(500, 100))\n",
@@ -737,22 +622,7 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Batch accuracy: 0.96 Validation accuracy: 0.924\n",
-      "5 Batch accuracy: 1.0 Validation accuracy: 0.9568\n",
-      "10 Batch accuracy: 0.94 Validation accuracy: 0.9668\n",
-      "15 Batch accuracy: 0.98 Validation accuracy: 0.9684\n",
-      "20 Batch accuracy: 1.0 Validation accuracy: 0.9712\n",
-      "25 Batch accuracy: 1.0 Validation accuracy: 0.9694\n",
-      "30 Batch accuracy: 1.0 Validation accuracy: 0.97\n",
-      "35 Batch accuracy: 1.0 Validation accuracy: 0.971\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "means = mnist.train.images.mean(axis=0, keepdims=True)\n",
     "stds = mnist.train.images.std(axis=0, keepdims=True) + 1e-10\n",
@@ -954,34 +824,7 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Test accuracy: 0.8727\n",
-      "1 Test accuracy: 0.8981\n",
-      "2 Test accuracy: 0.9129\n",
-      "3 Test accuracy: 0.922\n",
-      "4 Test accuracy: 0.9292\n",
-      "5 Test accuracy: 0.9342\n",
-      "6 Test accuracy: 0.9381\n",
-      "7 Test accuracy: 0.9419\n",
-      "8 Test accuracy: 0.9451\n",
-      "9 Test accuracy: 0.9471\n",
-      "10 Test accuracy: 0.9507\n",
-      "11 Test accuracy: 0.9521\n",
-      "12 Test accuracy: 0.9553\n",
-      "13 Test accuracy: 0.956\n",
-      "14 Test accuracy: 0.957\n",
-      "15 Test accuracy: 0.9583\n",
-      "16 Test accuracy: 0.9613\n",
-      "17 Test accuracy: 0.9608\n",
-      "18 Test accuracy: 0.9627\n",
-      "19 Test accuracy: 0.963\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
     "\n",
@@ -1038,29 +881,7 @@
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['hidden1/kernel:0',\n",
-       " 'hidden1/bias:0',\n",
-       " 'batch_normalization/beta:0',\n",
-       " 'batch_normalization/gamma:0',\n",
-       " 'hidden2/kernel:0',\n",
-       " 'hidden2/bias:0',\n",
-       " 'batch_normalization_1/beta:0',\n",
-       " 'batch_normalization_1/gamma:0',\n",
-       " 'outputs/kernel:0',\n",
-       " 'outputs/bias:0',\n",
-       " 'batch_normalization_2/beta:0',\n",
-       " 'batch_normalization_2/gamma:0']"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "[v.name for v in tf.trainable_variables()]"
    ]
@@ -1069,35 +890,7 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['hidden1/kernel:0',\n",
-       " 'hidden1/bias:0',\n",
-       " 'batch_normalization/beta:0',\n",
-       " 'batch_normalization/gamma:0',\n",
-       " 'batch_normalization/moving_mean:0',\n",
-       " 'batch_normalization/moving_variance:0',\n",
-       " 'hidden2/kernel:0',\n",
-       " 'hidden2/bias:0',\n",
-       " 'batch_normalization_1/beta:0',\n",
-       " 'batch_normalization_1/gamma:0',\n",
-       " 'batch_normalization_1/moving_mean:0',\n",
-       " 'batch_normalization_1/moving_variance:0',\n",
-       " 'outputs/kernel:0',\n",
-       " 'outputs/bias:0',\n",
-       " 'batch_normalization_2/beta:0',\n",
-       " 'batch_normalization_2/gamma:0',\n",
-       " 'batch_normalization_2/moving_mean:0',\n",
-       " 'batch_normalization_2/moving_variance:0']"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "[v.name for v in tf.global_variables()]"
    ]
@@ -1233,34 +1026,7 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Test accuracy: 0.3139\n",
-      "1 Test accuracy: 0.8001\n",
-      "2 Test accuracy: 0.8806\n",
-      "3 Test accuracy: 0.9037\n",
-      "4 Test accuracy: 0.9124\n",
-      "5 Test accuracy: 0.9197\n",
-      "6 Test accuracy: 0.9243\n",
-      "7 Test accuracy: 0.9299\n",
-      "8 Test accuracy: 0.9331\n",
-      "9 Test accuracy: 0.9387\n",
-      "10 Test accuracy: 0.9431\n",
-      "11 Test accuracy: 0.9445\n",
-      "12 Test accuracy: 0.9455\n",
-      "13 Test accuracy: 0.9485\n",
-      "14 Test accuracy: 0.9524\n",
-      "15 Test accuracy: 0.9511\n",
-      "16 Test accuracy: 0.9562\n",
-      "17 Test accuracy: 0.9583\n",
-      "18 Test accuracy: 0.9559\n",
-      "19 Test accuracy: 0.9605\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with tf.Session() as sess:\n",
     "    init.run()\n",
@@ -1329,324 +1095,7 @@
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X\n",
-      "y\n",
-      "hidden1/kernel/Initializer/random_uniform/shape\n",
-      "hidden1/kernel/Initializer/random_uniform/min\n",
-      "hidden1/kernel/Initializer/random_uniform/max\n",
-      "hidden1/kernel/Initializer/random_uniform/RandomUniform\n",
-      "hidden1/kernel/Initializer/random_uniform/sub\n",
-      "hidden1/kernel/Initializer/random_uniform/mul\n",
-      "hidden1/kernel/Initializer/random_uniform\n",
-      "hidden1/kernel\n",
-      "hidden1/kernel/Assign\n",
-      "hidden1/kernel/read\n",
-      "hidden1/bias/Initializer/Const\n",
-      "hidden1/bias\n",
-      "hidden1/bias/Assign\n",
-      "hidden1/bias/read\n",
-      "dnn/hidden1/MatMul\n",
-      "dnn/hidden1/BiasAdd\n",
-      "dnn/hidden1/Relu\n",
-      "hidden2/kernel/Initializer/random_uniform/shape\n",
-      "hidden2/kernel/Initializer/random_uniform/min\n",
-      "hidden2/kernel/Initializer/random_uniform/max\n",
-      "hidden2/kernel/Initializer/random_uniform/RandomUniform\n",
-      "hidden2/kernel/Initializer/random_uniform/sub\n",
-      "hidden2/kernel/Initializer/random_uniform/mul\n",
-      "hidden2/kernel/Initializer/random_uniform\n",
-      "hidden2/kernel\n",
-      "hidden2/kernel/Assign\n",
-      "hidden2/kernel/read\n",
-      "hidden2/bias/Initializer/Const\n",
-      "hidden2/bias\n",
-      "hidden2/bias/Assign\n",
-      "hidden2/bias/read\n",
-      "dnn/hidden2/MatMul\n",
-      "dnn/hidden2/BiasAdd\n",
-      "dnn/hidden2/Relu\n",
-      "hidden3/kernel/Initializer/random_uniform/shape\n",
-      "hidden3/kernel/Initializer/random_uniform/min\n",
-      "hidden3/kernel/Initializer/random_uniform/max\n",
-      "hidden3/kernel/Initializer/random_uniform/RandomUniform\n",
-      "hidden3/kernel/Initializer/random_uniform/sub\n",
-      "hidden3/kernel/Initializer/random_uniform/mul\n",
-      "hidden3/kernel/Initializer/random_uniform\n",
-      "hidden3/kernel\n",
-      "hidden3/kernel/Assign\n",
-      "hidden3/kernel/read\n",
-      "hidden3/bias/Initializer/Const\n",
-      "hidden3/bias\n",
-      "hidden3/bias/Assign\n",
-      "hidden3/bias/read\n",
-      "dnn/hidden3/MatMul\n",
-      "dnn/hidden3/BiasAdd\n",
-      "dnn/hidden3/Relu\n",
-      "hidden4/kernel/Initializer/random_uniform/shape\n",
-      "hidden4/kernel/Initializer/random_uniform/min\n",
-      "hidden4/kernel/Initializer/random_uniform/max\n",
-      "hidden4/kernel/Initializer/random_uniform/RandomUniform\n",
-      "hidden4/kernel/Initializer/random_uniform/sub\n",
-      "hidden4/kernel/Initializer/random_uniform/mul\n",
-      "hidden4/kernel/Initializer/random_uniform\n",
-      "hidden4/kernel\n",
-      "hidden4/kernel/Assign\n",
-      "hidden4/kernel/read\n",
-      "hidden4/bias/Initializer/Const\n",
-      "hidden4/bias\n",
-      "hidden4/bias/Assign\n",
-      "hidden4/bias/read\n",
-      "dnn/hidden4/MatMul\n",
-      "dnn/hidden4/BiasAdd\n",
-      "dnn/hidden4/Relu\n",
-      "hidden5/kernel/Initializer/random_uniform/shape\n",
-      "hidden5/kernel/Initializer/random_uniform/min\n",
-      "hidden5/kernel/Initializer/random_uniform/max\n",
-      "hidden5/kernel/Initializer/random_uniform/RandomUniform\n",
-      "hidden5/kernel/Initializer/random_uniform/sub\n",
-      "hidden5/kernel/Initializer/random_uniform/mul\n",
-      "hidden5/kernel/Initializer/random_uniform\n",
-      "hidden5/kernel\n",
-      "hidden5/kernel/Assign\n",
-      "hidden5/kernel/read\n",
-      "hidden5/bias/Initializer/Const\n",
-      "hidden5/bias\n",
-      "hidden5/bias/Assign\n",
-      "hidden5/bias/read\n",
-      "dnn/hidden5/MatMul\n",
-      "dnn/hidden5/BiasAdd\n",
-      "dnn/hidden5/Relu\n",
-      "outputs/kernel/Initializer/random_uniform/shape\n",
-      "outputs/kernel/Initializer/random_uniform/min\n",
-      "outputs/kernel/Initializer/random_uniform/max\n",
-      "outputs/kernel/Initializer/random_uniform/RandomUniform\n",
-      "outputs/kernel/Initializer/random_uniform/sub\n",
-      "outputs/kernel/Initializer/random_uniform/mul\n",
-      "outputs/kernel/Initializer/random_uniform\n",
-      "outputs/kernel\n",
-      "outputs/kernel/Assign\n",
-      "outputs/kernel/read\n",
-      "outputs/bias/Initializer/Const\n",
-      "outputs/bias\n",
-      "outputs/bias/Assign\n",
-      "outputs/bias/read\n",
-      "dnn/outputs/MatMul\n",
-      "dnn/outputs/BiasAdd\n",
-      "loss/SparseSoftmaxCrossEntropyWithLogits/Shape\n",
-      "loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits\n",
-      "loss/Const\n",
-      "loss/loss\n",
-      "gradients/Shape\n",
-      "gradients/Const\n",
-      "gradients/Fill\n",
-      "gradients/loss/loss_grad/Reshape/shape\n",
-      "gradients/loss/loss_grad/Reshape\n",
-      "gradients/loss/loss_grad/Shape\n",
-      "gradients/loss/loss_grad/Tile\n",
-      "gradients/loss/loss_grad/Shape_1\n",
-      "gradients/loss/loss_grad/Shape_2\n",
-      "gradients/loss/loss_grad/Const\n",
-      "gradients/loss/loss_grad/Prod\n",
-      "gradients/loss/loss_grad/Const_1\n",
-      "gradients/loss/loss_grad/Prod_1\n",
-      "gradients/loss/loss_grad/Maximum/y\n",
-      "gradients/loss/loss_grad/Maximum\n",
-      "gradients/loss/loss_grad/floordiv\n",
-      "gradients/loss/loss_grad/Cast\n",
-      "gradients/loss/loss_grad/truediv\n",
-      "gradients/zeros_like\n",
-      "gradients/loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits_grad/PreventGradient\n",
-      "gradients/loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits_grad/ExpandDims/dim\n",
-      "gradients/loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits_grad/ExpandDims\n",
-      "gradients/loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits_grad/mul\n",
-      "gradients/dnn/outputs/BiasAdd_grad/BiasAddGrad\n",
-      "gradients/dnn/outputs/BiasAdd_grad/tuple/group_deps\n",
-      "gradients/dnn/outputs/BiasAdd_grad/tuple/control_dependency\n",
-      "gradients/dnn/outputs/BiasAdd_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/outputs/MatMul_grad/MatMul\n",
-      "gradients/dnn/outputs/MatMul_grad/MatMul_1\n",
-      "gradients/dnn/outputs/MatMul_grad/tuple/group_deps\n",
-      "gradients/dnn/outputs/MatMul_grad/tuple/control_dependency\n",
-      "gradients/dnn/outputs/MatMul_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/hidden5/Relu_grad/ReluGrad\n",
-      "gradients/dnn/hidden5/BiasAdd_grad/BiasAddGrad\n",
-      "gradients/dnn/hidden5/BiasAdd_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden5/BiasAdd_grad/tuple/control_dependency\n",
-      "gradients/dnn/hidden5/BiasAdd_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/hidden5/MatMul_grad/MatMul\n",
-      "gradients/dnn/hidden5/MatMul_grad/MatMul_1\n",
-      "gradients/dnn/hidden5/MatMul_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden5/MatMul_grad/tuple/control_dependency\n",
-      "gradients/dnn/hidden5/MatMul_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/hidden4/Relu_grad/ReluGrad\n",
-      "gradients/dnn/hidden4/BiasAdd_grad/BiasAddGrad\n",
-      "gradients/dnn/hidden4/BiasAdd_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden4/BiasAdd_grad/tuple/control_dependency\n",
-      "gradients/dnn/hidden4/BiasAdd_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/hidden4/MatMul_grad/MatMul\n",
-      "gradients/dnn/hidden4/MatMul_grad/MatMul_1\n",
-      "gradients/dnn/hidden4/MatMul_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden4/MatMul_grad/tuple/control_dependency\n",
-      "gradients/dnn/hidden4/MatMul_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/hidden3/Relu_grad/ReluGrad\n",
-      "gradients/dnn/hidden3/BiasAdd_grad/BiasAddGrad\n",
-      "gradients/dnn/hidden3/BiasAdd_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden3/BiasAdd_grad/tuple/control_dependency\n",
-      "gradients/dnn/hidden3/BiasAdd_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/hidden3/MatMul_grad/MatMul\n",
-      "gradients/dnn/hidden3/MatMul_grad/MatMul_1\n",
-      "gradients/dnn/hidden3/MatMul_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden3/MatMul_grad/tuple/control_dependency\n",
-      "gradients/dnn/hidden3/MatMul_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/hidden2/Relu_grad/ReluGrad\n",
-      "gradients/dnn/hidden2/BiasAdd_grad/BiasAddGrad\n",
-      "gradients/dnn/hidden2/BiasAdd_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden2/BiasAdd_grad/tuple/control_dependency\n",
-      "gradients/dnn/hidden2/BiasAdd_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/hidden2/MatMul_grad/MatMul\n",
-      "gradients/dnn/hidden2/MatMul_grad/MatMul_1\n",
-      "gradients/dnn/hidden2/MatMul_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden2/MatMul_grad/tuple/control_dependency\n",
-      "gradients/dnn/hidden2/MatMul_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/hidden1/Relu_grad/ReluGrad\n",
-      "gradients/dnn/hidden1/BiasAdd_grad/BiasAddGrad\n",
-      "gradients/dnn/hidden1/BiasAdd_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden1/BiasAdd_grad/tuple/control_dependency\n",
-      "gradients/dnn/hidden1/BiasAdd_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/hidden1/MatMul_grad/MatMul\n",
-      "gradients/dnn/hidden1/MatMul_grad/MatMul_1\n",
-      "gradients/dnn/hidden1/MatMul_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden1/MatMul_grad/tuple/control_dependency\n",
-      "gradients/dnn/hidden1/MatMul_grad/tuple/control_dependency_1\n",
-      "clip_by_value/Minimum/y\n",
-      "clip_by_value/Minimum\n",
-      "clip_by_value/y\n",
-      "clip_by_value\n",
-      "clip_by_value_1/Minimum/y\n",
-      "clip_by_value_1/Minimum\n",
-      "clip_by_value_1/y\n",
-      "clip_by_value_1\n",
-      "clip_by_value_2/Minimum/y\n",
-      "clip_by_value_2/Minimum\n",
-      "clip_by_value_2/y\n",
-      "clip_by_value_2\n",
-      "clip_by_value_3/Minimum/y\n",
-      "clip_by_value_3/Minimum\n",
-      "clip_by_value_3/y\n",
-      "clip_by_value_3\n",
-      "clip_by_value_4/Minimum/y\n",
-      "clip_by_value_4/Minimum\n",
-      "clip_by_value_4/y\n",
-      "clip_by_value_4\n",
-      "clip_by_value_5/Minimum/y\n",
-      "clip_by_value_5/Minimum\n",
-      "clip_by_value_5/y\n",
-      "clip_by_value_5\n",
-      "clip_by_value_6/Minimum/y\n",
-      "clip_by_value_6/Minimum\n",
-      "clip_by_value_6/y\n",
-      "clip_by_value_6\n",
-      "clip_by_value_7/Minimum/y\n",
-      "clip_by_value_7/Minimum\n",
-      "clip_by_value_7/y\n",
-      "clip_by_value_7\n",
-      "clip_by_value_8/Minimum/y\n",
-      "clip_by_value_8/Minimum\n",
-      "clip_by_value_8/y\n",
-      "clip_by_value_8\n",
-      "clip_by_value_9/Minimum/y\n",
-      "clip_by_value_9/Minimum\n",
-      "clip_by_value_9/y\n",
-      "clip_by_value_9\n",
-      "clip_by_value_10/Minimum/y\n",
-      "clip_by_value_10/Minimum\n",
-      "clip_by_value_10/y\n",
-      "clip_by_value_10\n",
-      "clip_by_value_11/Minimum/y\n",
-      "clip_by_value_11/Minimum\n",
-      "clip_by_value_11/y\n",
-      "clip_by_value_11\n",
-      "GradientDescent/learning_rate\n",
-      "GradientDescent/update_hidden1/kernel/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden1/bias/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden2/kernel/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden2/bias/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden3/kernel/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden3/bias/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden4/kernel/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden4/bias/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden5/kernel/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden5/bias/ApplyGradientDescent\n",
-      "GradientDescent/update_outputs/kernel/ApplyGradientDescent\n",
-      "GradientDescent/update_outputs/bias/ApplyGradientDescent\n",
-      "GradientDescent\n",
-      "eval/InTopK\n",
-      "eval/Cast\n",
-      "eval/Const\n",
-      "eval/accuracy\n",
-      "init\n",
-      "save/Const\n",
-      "save/SaveV2/tensor_names\n",
-      "save/SaveV2/shape_and_slices\n",
-      "save/SaveV2\n",
-      "save/control_dependency\n",
-      "save/RestoreV2/tensor_names\n",
-      "save/RestoreV2/shape_and_slices\n",
-      "save/RestoreV2\n",
-      "save/Assign\n",
-      "save/RestoreV2_1/tensor_names\n",
-      "save/RestoreV2_1/shape_and_slices\n",
-      "save/RestoreV2_1\n",
-      "save/Assign_1\n",
-      "save/RestoreV2_2/tensor_names\n",
-      "save/RestoreV2_2/shape_and_slices\n",
-      "save/RestoreV2_2\n",
-      "save/Assign_2\n",
-      "save/RestoreV2_3/tensor_names\n",
-      "save/RestoreV2_3/shape_and_slices\n",
-      "save/RestoreV2_3\n",
-      "save/Assign_3\n",
-      "save/RestoreV2_4/tensor_names\n",
-      "save/RestoreV2_4/shape_and_slices\n",
-      "save/RestoreV2_4\n",
-      "save/Assign_4\n",
-      "save/RestoreV2_5/tensor_names\n",
-      "save/RestoreV2_5/shape_and_slices\n",
-      "save/RestoreV2_5\n",
-      "save/Assign_5\n",
-      "save/RestoreV2_6/tensor_names\n",
-      "save/RestoreV2_6/shape_and_slices\n",
-      "save/RestoreV2_6\n",
-      "save/Assign_6\n",
-      "save/RestoreV2_7/tensor_names\n",
-      "save/RestoreV2_7/shape_and_slices\n",
-      "save/RestoreV2_7\n",
-      "save/Assign_7\n",
-      "save/RestoreV2_8/tensor_names\n",
-      "save/RestoreV2_8/shape_and_slices\n",
-      "save/RestoreV2_8\n",
-      "save/Assign_8\n",
-      "save/RestoreV2_9/tensor_names\n",
-      "save/RestoreV2_9/shape_and_slices\n",
-      "save/RestoreV2_9\n",
-      "save/Assign_9\n",
-      "save/RestoreV2_10/tensor_names\n",
-      "save/RestoreV2_10/shape_and_slices\n",
-      "save/RestoreV2_10\n",
-      "save/Assign_10\n",
-      "save/RestoreV2_11/tensor_names\n",
-      "save/RestoreV2_11/shape_and_slices\n",
-      "save/RestoreV2_11\n",
-      "save/Assign_11\n",
-      "save/restore_all\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for op in tf.get_default_graph().get_operations():\n",
     "    print(op.name)"
@@ -1711,32 +1160,7 @@
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-   "outputs": [
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&quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: &quot;hidden4/kernel&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_7/shape_and_slices&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: &quot;&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_7&quot;\\n  op: &quot;RestoreV2&quot;\\n  input: &quot;save/Const&quot;\\n  input: &quot;save/RestoreV2_7/tensor_names&quot;\\n  input: &quot;save/RestoreV2_7/shape_and_slices&quot;\\n  attr {\\n    key: &quot;dtypes&quot;\\n    value {\\n      list {\\n        type: DT_FLOAT\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/Assign_7&quot;\\n  op: &quot;Assign&quot;\\n  input: &quot;hidden4/kernel&quot;\\n  input: &quot;save/RestoreV2_7&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@hidden4/kernel&quot;\\n      }\\n    }\\n  }\\n  attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n  attr {\\n    key: &quot;validate_shape&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_8/tensor_names&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: &quot;hidden5/bias&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_8/shape_and_slices&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: &quot;&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_8&quot;\\n  op: &quot;RestoreV2&quot;\\n  input: &quot;save/Const&quot;\\n  input: &quot;save/RestoreV2_8/tensor_names&quot;\\n  input: &quot;save/RestoreV2_8/shape_and_slices&quot;\\n  attr {\\n    key: &quot;dtypes&quot;\\n    value {\\n      list {\\n        type: DT_FLOAT\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/Assign_8&quot;\\n  op: &quot;Assign&quot;\\n  input: &quot;hidden5/bias&quot;\\n  input: &quot;save/RestoreV2_8&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@hidden5/bias&quot;\\n      }\\n    }\\n  }\\n  attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n  attr {\\n    key: &quot;validate_shape&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_9/tensor_names&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: &quot;hidden5/kernel&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_9/shape_and_slices&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: &quot;&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_9&quot;\\n  op: &quot;RestoreV2&quot;\\n  input: &quot;save/Const&quot;\\n  input: &quot;save/RestoreV2_9/tensor_names&quot;\\n  input: &quot;save/RestoreV2_9/shape_and_slices&quot;\\n  attr {\\n    key: &quot;dtypes&quot;\\n    value {\\n      list {\\n        type: DT_FLOAT\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/Assign_9&quot;\\n  op: &quot;Assign&quot;\\n  input: &quot;hidden5/kernel&quot;\\n  input: &quot;save/RestoreV2_9&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@hidden5/kernel&quot;\\n      }\\n    }\\n  }\\n  attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n  attr {\\n    key: &quot;validate_shape&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_10/tensor_names&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: &quot;outputs/bias&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_10/shape_and_slices&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: &quot;&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_10&quot;\\n  op: &quot;RestoreV2&quot;\\n  input: &quot;save/Const&quot;\\n  input: &quot;save/RestoreV2_10/tensor_names&quot;\\n  input: &quot;save/RestoreV2_10/shape_and_slices&quot;\\n  attr {\\n    key: &quot;dtypes&quot;\\n    value {\\n      list {\\n        type: DT_FLOAT\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/Assign_10&quot;\\n  op: &quot;Assign&quot;\\n  input: &quot;outputs/bias&quot;\\n  input: &quot;save/RestoreV2_10&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@outputs/bias&quot;\\n      }\\n    }\\n  }\\n  attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n  attr {\\n    key: &quot;validate_shape&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_11/tensor_names&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: &quot;outputs/kernel&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_11/shape_and_slices&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: &quot;&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2_11&quot;\\n  op: &quot;RestoreV2&quot;\\n  input: &quot;save/Const&quot;\\n  input: &quot;save/RestoreV2_11/tensor_names&quot;\\n  input: &quot;save/RestoreV2_11/shape_and_slices&quot;\\n  attr {\\n    key: &quot;dtypes&quot;\\n    value {\\n      list {\\n        type: DT_FLOAT\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/Assign_11&quot;\\n  op: &quot;Assign&quot;\\n  input: &quot;outputs/kernel&quot;\\n  input: &quot;save/RestoreV2_11&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@outputs/kernel&quot;\\n      }\\n    }\\n  }\\n  attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n  attr {\\n    key: &quot;validate_shape&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/restore_all&quot;\\n  op: &quot;NoOp&quot;\\n  input: &quot;^save/Assign&quot;\\n  input: &quot;^save/Assign_1&quot;\\n  input: &quot;^save/Assign_2&quot;\\n  input: &quot;^save/Assign_3&quot;\\n  input: &quot;^save/Assign_4&quot;\\n  input: &quot;^save/Assign_5&quot;\\n  input: &quot;^save/Assign_6&quot;\\n  input: &quot;^save/Assign_7&quot;\\n  input: &quot;^save/Assign_8&quot;\\n  input: &quot;^save/Assign_9&quot;\\n  input: &quot;^save/Assign_10&quot;\\n  input: &quot;^save/Assign_11&quot;\\n}\\n';\n",
-       "          }\n",
-       "        </script>\n",
-       "        <link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n",
-       "        <div style=&quot;height:600px&quot;>\n",
-       "          <tf-graph-basic id=&quot;graph0.3745401188473625&quot;></tf-graph-basic>\n",
-       "        </div>\n",
-       "    \"></iframe>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_graph(tf.get_default_graph())"
    ]
@@ -1812,15 +1236,7 @@
    "cell_type": "code",
    "execution_count": 53,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with tf.Session() as sess:\n",
     "    saver.restore(sess, \"./my_model_final.ckpt\")\n",
@@ -1838,35 +1254,7 @@
    "cell_type": "code",
    "execution_count": 54,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 Test accuracy: 0.9609\n",
-      "1 Test accuracy: 0.9608\n",
-      "2 Test accuracy: 0.9617\n",
-      "3 Test accuracy: 0.9613\n",
-      "4 Test accuracy: 0.9639\n",
-      "5 Test accuracy: 0.9649\n",
-      "6 Test accuracy: 0.9663\n",
-      "7 Test accuracy: 0.9627\n",
-      "8 Test accuracy: 0.9665\n",
-      "9 Test accuracy: 0.9669\n",
-      "10 Test accuracy: 0.9662\n",
-      "11 Test accuracy: 0.9674\n",
-      "12 Test accuracy: 0.9678\n",
-      "13 Test accuracy: 0.9679\n",
-      "14 Test accuracy: 0.9688\n",
-      "15 Test accuracy: 0.9684\n",
-      "16 Test accuracy: 0.9687\n",
-      "17 Test accuracy: 0.9702\n",
-      "18 Test accuracy: 0.9673\n",
-      "19 Test accuracy: 0.9687\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with tf.Session() as sess:\n",
     "    saver.restore(sess, \"./my_model_final.ckpt\")\n",
@@ -1949,35 +1337,7 @@
    "cell_type": "code",
    "execution_count": 56,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 Test accuracy: 0.9611\n",
-      "1 Test accuracy: 0.9619\n",
-      "2 Test accuracy: 0.9622\n",
-      "3 Test accuracy: 0.9619\n",
-      "4 Test accuracy: 0.9644\n",
-      "5 Test accuracy: 0.9633\n",
-      "6 Test accuracy: 0.9647\n",
-      "7 Test accuracy: 0.9648\n",
-      "8 Test accuracy: 0.9671\n",
-      "9 Test accuracy: 0.9677\n",
-      "10 Test accuracy: 0.9676\n",
-      "11 Test accuracy: 0.9679\n",
-      "12 Test accuracy: 0.9687\n",
-      "13 Test accuracy: 0.9688\n",
-      "14 Test accuracy: 0.9683\n",
-      "15 Test accuracy: 0.9693\n",
-      "16 Test accuracy: 0.9677\n",
-      "17 Test accuracy: 0.9697\n",
-      "18 Test accuracy: 0.9692\n",
-      "19 Test accuracy: 0.9707\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with tf.Session() as sess:\n",
     "    saver.restore(sess, \"./my_model_final.ckpt\")\n",
@@ -2050,35 +1410,7 @@
    "cell_type": "code",
    "execution_count": 58,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 Test accuracy: 0.9142\n",
-      "1 Test accuracy: 0.9346\n",
-      "2 Test accuracy: 0.9437\n",
-      "3 Test accuracy: 0.9486\n",
-      "4 Test accuracy: 0.9517\n",
-      "5 Test accuracy: 0.9544\n",
-      "6 Test accuracy: 0.9544\n",
-      "7 Test accuracy: 0.9562\n",
-      "8 Test accuracy: 0.9588\n",
-      "9 Test accuracy: 0.9619\n",
-      "10 Test accuracy: 0.9617\n",
-      "11 Test accuracy: 0.9617\n",
-      "12 Test accuracy: 0.9624\n",
-      "13 Test accuracy: 0.9644\n",
-      "14 Test accuracy: 0.9622\n",
-      "15 Test accuracy: 0.964\n",
-      "16 Test accuracy: 0.9666\n",
-      "17 Test accuracy: 0.9668\n",
-      "18 Test accuracy: 0.9673\n",
-      "19 Test accuracy: 0.9687\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with tf.Session() as sess:\n",
     "    init.run()\n",
@@ -2153,35 +1485,7 @@
    "cell_type": "code",
    "execution_count": 60,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 Test accuracy: 0.9022\n",
-      "1 Test accuracy: 0.9302\n",
-      "2 Test accuracy: 0.9393\n",
-      "3 Test accuracy: 0.9429\n",
-      "4 Test accuracy: 0.9484\n",
-      "5 Test accuracy: 0.9511\n",
-      "6 Test accuracy: 0.9517\n",
-      "7 Test accuracy: 0.9539\n",
-      "8 Test accuracy: 0.9545\n",
-      "9 Test accuracy: 0.9572\n",
-      "10 Test accuracy: 0.9599\n",
-      "11 Test accuracy: 0.9602\n",
-      "12 Test accuracy: 0.9606\n",
-      "13 Test accuracy: 0.9619\n",
-      "14 Test accuracy: 0.9619\n",
-      "15 Test accuracy: 0.9636\n",
-      "16 Test accuracy: 0.9633\n",
-      "17 Test accuracy: 0.9643\n",
-      "18 Test accuracy: 0.9651\n",
-      "19 Test accuracy: 0.9657\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "reuse_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\n",
     "                               scope=\"hidden[123]\") # regular expression\n",
@@ -2238,15 +1542,7 @@
    "cell_type": "code",
    "execution_count": 62,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[  61.   83.  105.]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "original_w = [[1., 2., 3.], [4., 5., 6.]] # Load the weights from the other framework\n",
     "original_b = [7., 8., 9.]                 # Load the biases from the other framework\n",
@@ -2288,15 +1584,7 @@
    "cell_type": "code",
    "execution_count": 63,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[  61.   83.  105.]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "reset_graph()\n",
     "\n",
@@ -2342,19 +1630,7 @@
    "cell_type": "code",
    "execution_count": 64,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<tf.Variable 'hidden1/kernel:0' shape=(2, 3) dtype=float32_ref>,\n",
-       " <tf.Variable 'hidden1/bias:0' shape=(3,) dtype=float32_ref>]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=\"hidden1\")"
    ]
@@ -2370,18 +1646,7 @@
    "cell_type": "code",
    "execution_count": 65,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor 'hidden1/kernel:0' shape=(2, 3) dtype=float32_ref>"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tf.get_default_graph().get_tensor_by_name(\"hidden1/kernel:0\")"
    ]
@@ -2390,18 +1655,7 @@
    "cell_type": "code",
    "execution_count": 66,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor 'hidden1/bias:0' shape=(3,) dtype=float32_ref>"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tf.get_default_graph().get_tensor_by_name(\"hidden1/bias:0\")"
    ]
@@ -2480,35 +1734,7 @@
    "cell_type": "code",
    "execution_count": 70,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 Test accuracy: 0.8987\n",
-      "1 Test accuracy: 0.9311\n",
-      "2 Test accuracy: 0.9375\n",
-      "3 Test accuracy: 0.9414\n",
-      "4 Test accuracy: 0.9437\n",
-      "5 Test accuracy: 0.9479\n",
-      "6 Test accuracy: 0.9495\n",
-      "7 Test accuracy: 0.9521\n",
-      "8 Test accuracy: 0.9517\n",
-      "9 Test accuracy: 0.9525\n",
-      "10 Test accuracy: 0.9535\n",
-      "11 Test accuracy: 0.9538\n",
-      "12 Test accuracy: 0.9534\n",
-      "13 Test accuracy: 0.9546\n",
-      "14 Test accuracy: 0.9538\n",
-      "15 Test accuracy: 0.9553\n",
-      "16 Test accuracy: 0.9552\n",
-      "17 Test accuracy: 0.9549\n",
-      "18 Test accuracy: 0.9553\n",
-      "19 Test accuracy: 0.9557\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "reuse_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\n",
     "                               scope=\"hidden[123]\") # regular expression\n",
@@ -2607,35 +1833,7 @@
    "cell_type": "code",
    "execution_count": 74,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 Test accuracy: 0.9031\n",
-      "1 Test accuracy: 0.932\n",
-      "2 Test accuracy: 0.94\n",
-      "3 Test accuracy: 0.9435\n",
-      "4 Test accuracy: 0.9473\n",
-      "5 Test accuracy: 0.9492\n",
-      "6 Test accuracy: 0.9498\n",
-      "7 Test accuracy: 0.9493\n",
-      "8 Test accuracy: 0.9515\n",
-      "9 Test accuracy: 0.9519\n",
-      "10 Test accuracy: 0.9529\n",
-      "11 Test accuracy: 0.9536\n",
-      "12 Test accuracy: 0.9529\n",
-      "13 Test accuracy: 0.9532\n",
-      "14 Test accuracy: 0.9522\n",
-      "15 Test accuracy: 0.9534\n",
-      "16 Test accuracy: 0.953\n",
-      "17 Test accuracy: 0.955\n",
-      "18 Test accuracy: 0.955\n",
-      "19 Test accuracy: 0.9552\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "reuse_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\n",
     "                               scope=\"hidden[123]\") # regular expression\n",
@@ -2733,35 +1931,7 @@
    "cell_type": "code",
    "execution_count": 77,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 Test accuracy: 0.9033\n",
-      "1 Test accuracy: 0.9322\n",
-      "2 Test accuracy: 0.9423\n",
-      "3 Test accuracy: 0.9449\n",
-      "4 Test accuracy: 0.9471\n",
-      "5 Test accuracy: 0.9477\n",
-      "6 Test accuracy: 0.951\n",
-      "7 Test accuracy: 0.9507\n",
-      "8 Test accuracy: 0.9514\n",
-      "9 Test accuracy: 0.9522\n",
-      "10 Test accuracy: 0.9512\n",
-      "11 Test accuracy: 0.9521\n",
-      "12 Test accuracy: 0.9522\n",
-      "13 Test accuracy: 0.9539\n",
-      "14 Test accuracy: 0.9536\n",
-      "15 Test accuracy: 0.9534\n",
-      "16 Test accuracy: 0.9547\n",
-      "17 Test accuracy: 0.9537\n",
-      "18 Test accuracy: 0.9542\n",
-      "19 Test accuracy: 0.9547\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -2962,19 +2132,7 @@
    "cell_type": "code",
    "execution_count": 86,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Test accuracy: 0.9579\n",
-      "1 Test accuracy: 0.9691\n",
-      "2 Test accuracy: 0.976\n",
-      "3 Test accuracy: 0.9793\n",
-      "4 Test accuracy: 0.9811\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 5\n",
     "batch_size = 50\n",
@@ -3098,34 +2256,7 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Test accuracy: 0.8343\n",
-      "1 Test accuracy: 0.8726\n",
-      "2 Test accuracy: 0.8832\n",
-      "3 Test accuracy: 0.8899\n",
-      "4 Test accuracy: 0.8958\n",
-      "5 Test accuracy: 0.8986\n",
-      "6 Test accuracy: 0.9011\n",
-      "7 Test accuracy: 0.9032\n",
-      "8 Test accuracy: 0.9046\n",
-      "9 Test accuracy: 0.9047\n",
-      "10 Test accuracy: 0.9065\n",
-      "11 Test accuracy: 0.9059\n",
-      "12 Test accuracy: 0.9072\n",
-      "13 Test accuracy: 0.9072\n",
-      "14 Test accuracy: 0.9069\n",
-      "15 Test accuracy: 0.9071\n",
-      "16 Test accuracy: 0.9064\n",
-      "17 Test accuracy: 0.9071\n",
-      "18 Test accuracy: 0.9068\n",
-      "19 Test accuracy: 0.9063\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 20\n",
     "batch_size = 200\n",
@@ -3264,34 +2395,7 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Test accuracy: 0.8298\n",
-      "1 Test accuracy: 0.8778\n",
-      "2 Test accuracy: 0.8917\n",
-      "3 Test accuracy: 0.9017\n",
-      "4 Test accuracy: 0.9068\n",
-      "5 Test accuracy: 0.9103\n",
-      "6 Test accuracy: 0.9125\n",
-      "7 Test accuracy: 0.9137\n",
-      "8 Test accuracy: 0.9149\n",
-      "9 Test accuracy: 0.9174\n",
-      "10 Test accuracy: 0.9176\n",
-      "11 Test accuracy: 0.9184\n",
-      "12 Test accuracy: 0.9191\n",
-      "13 Test accuracy: 0.9183\n",
-      "14 Test accuracy: 0.9195\n",
-      "15 Test accuracy: 0.9201\n",
-      "16 Test accuracy: 0.9181\n",
-      "17 Test accuracy: 0.9184\n",
-      "18 Test accuracy: 0.9181\n",
-      "19 Test accuracy: 0.9174\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 20\n",
     "batch_size = 200\n",
@@ -3392,34 +2496,7 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Test accuracy: 0.9205\n",
-      "1 Test accuracy: 0.9418\n",
-      "2 Test accuracy: 0.9486\n",
-      "3 Test accuracy: 0.9508\n",
-      "4 Test accuracy: 0.954\n",
-      "5 Test accuracy: 0.957\n",
-      "6 Test accuracy: 0.9604\n",
-      "7 Test accuracy: 0.9585\n",
-      "8 Test accuracy: 0.9598\n",
-      "9 Test accuracy: 0.9663\n",
-      "10 Test accuracy: 0.9644\n",
-      "11 Test accuracy: 0.9646\n",
-      "12 Test accuracy: 0.9675\n",
-      "13 Test accuracy: 0.9657\n",
-      "14 Test accuracy: 0.9645\n",
-      "15 Test accuracy: 0.9668\n",
-      "16 Test accuracy: 0.969\n",
-      "17 Test accuracy: 0.9682\n",
-      "18 Test accuracy: 0.9698\n",
-      "19 Test accuracy: 0.9682\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 20\n",
     "batch_size = 50\n",
@@ -3572,34 +2649,7 @@
    "cell_type": "code",
    "execution_count": 106,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Test accuracy: 0.9517\n",
-      "1 Test accuracy: 0.9674\n",
-      "2 Test accuracy: 0.9712\n",
-      "3 Test accuracy: 0.9759\n",
-      "4 Test accuracy: 0.975\n",
-      "5 Test accuracy: 0.9761\n",
-      "6 Test accuracy: 0.9765\n",
-      "7 Test accuracy: 0.9796\n",
-      "8 Test accuracy: 0.9791\n",
-      "9 Test accuracy: 0.9794\n",
-      "10 Test accuracy: 0.9805\n",
-      "11 Test accuracy: 0.9809\n",
-      "12 Test accuracy: 0.9807\n",
-      "13 Test accuracy: 0.9799\n",
-      "14 Test accuracy: 0.982\n",
-      "15 Test accuracy: 0.9816\n",
-      "16 Test accuracy: 0.9825\n",
-      "17 Test accuracy: 0.9825\n",
-      "18 Test accuracy: 0.9816\n",
-      "19 Test accuracy: 0.9822\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with tf.Session() as sess:                                              # not shown in the book\n",
     "    init.run()                                                          # not shown\n",
@@ -3737,34 +2787,7 @@
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Test accuracy: 0.9527\n",
-      "1 Test accuracy: 0.9653\n",
-      "2 Test accuracy: 0.97\n",
-      "3 Test accuracy: 0.9751\n",
-      "4 Test accuracy: 0.9752\n",
-      "5 Test accuracy: 0.9742\n",
-      "6 Test accuracy: 0.9754\n",
-      "7 Test accuracy: 0.9784\n",
-      "8 Test accuracy: 0.9775\n",
-      "9 Test accuracy: 0.9789\n",
-      "10 Test accuracy: 0.9808\n",
-      "11 Test accuracy: 0.9797\n",
-      "12 Test accuracy: 0.9802\n",
-      "13 Test accuracy: 0.9799\n",
-      "14 Test accuracy: 0.9808\n",
-      "15 Test accuracy: 0.9809\n",
-      "16 Test accuracy: 0.9807\n",
-      "17 Test accuracy: 0.9803\n",
-      "18 Test accuracy: 0.9816\n",
-      "19 Test accuracy: 0.9812\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "clip_all_weights = tf.get_collection(\"max_norm\")\n",
     "\n",
@@ -3930,18 +2953,7 @@
    "cell_type": "code",
    "execution_count": 116,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
-      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
-      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
-      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from tensorflow.examples.tutorials.mnist import input_data\n",
     "mnist = input_data.read_data_sets(\"/tmp/data/\")"
@@ -3974,42 +2986,7 @@
    "cell_type": "code",
    "execution_count": 118,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\tValidation loss: 0.128663\tBest loss: 0.128663\tAccuracy: 96.64%\n",
-      "1\tValidation loss: 0.448317\tBest loss: 0.128663\tAccuracy: 78.19%\n",
-      "2\tValidation loss: 0.190859\tBest loss: 0.128663\tAccuracy: 95.54%\n",
-      "3\tValidation loss: 0.146951\tBest loss: 0.128663\tAccuracy: 96.79%\n",
-      "4\tValidation loss: 0.086076\tBest loss: 0.086076\tAccuracy: 97.69%\n",
-      "5\tValidation loss: 0.115353\tBest loss: 0.086076\tAccuracy: 97.77%\n",
-      "6\tValidation loss: 0.239142\tBest loss: 0.086076\tAccuracy: 95.15%\n",
-      "7\tValidation loss: 0.088810\tBest loss: 0.086076\tAccuracy: 98.12%\n",
-      "8\tValidation loss: 0.108763\tBest loss: 0.086076\tAccuracy: 97.81%\n",
-      "9\tValidation loss: 0.300808\tBest loss: 0.086076\tAccuracy: 96.17%\n",
-      "10\tValidation loss: 0.179260\tBest loss: 0.086076\tAccuracy: 97.46%\n",
-      "11\tValidation loss: 0.125690\tBest loss: 0.086076\tAccuracy: 98.48%\n",
-      "12\tValidation loss: 0.738371\tBest loss: 0.086076\tAccuracy: 77.72%\n",
-      "13\tValidation loss: 1.894743\tBest loss: 0.086076\tAccuracy: 78.54%\n",
-      "14\tValidation loss: 0.415678\tBest loss: 0.086076\tAccuracy: 78.50%\n",
-      "15\tValidation loss: 0.537646\tBest loss: 0.086076\tAccuracy: 75.45%\n",
-      "16\tValidation loss: 1.009708\tBest loss: 0.086076\tAccuracy: 53.99%\n",
-      "17\tValidation loss: 1.228350\tBest loss: 0.086076\tAccuracy: 38.15%\n",
-      "18\tValidation loss: 1.510606\tBest loss: 0.086076\tAccuracy: 29.44%\n",
-      "19\tValidation loss: 1.632344\tBest loss: 0.086076\tAccuracy: 22.01%\n",
-      "20\tValidation loss: 1.628246\tBest loss: 0.086076\tAccuracy: 22.01%\n",
-      "21\tValidation loss: 1.626765\tBest loss: 0.086076\tAccuracy: 22.01%\n",
-      "22\tValidation loss: 1.651615\tBest loss: 0.086076\tAccuracy: 18.73%\n",
-      "23\tValidation loss: 1.663751\tBest loss: 0.086076\tAccuracy: 19.27%\n",
-      "24\tValidation loss: 1.675138\tBest loss: 0.086076\tAccuracy: 22.01%\n",
-      "Early stopping!\n",
-      "INFO:tensorflow:Restoring parameters from ./my_mnist_model_0_to_4.ckpt\n",
-      "Final test accuracy: 98.05%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 1000\n",
     "batch_size = 20\n",
@@ -4277,56 +3254,7 @@
    "cell_type": "code",
    "execution_count": 120,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\tValidation loss: 0.128663\tBest loss: 0.128663\tAccuracy: 96.64%\n",
-      "1\tValidation loss: 0.448317\tBest loss: 0.128663\tAccuracy: 78.19%\n",
-      "2\tValidation loss: 0.190859\tBest loss: 0.128663\tAccuracy: 95.54%\n",
-      "3\tValidation loss: 0.146951\tBest loss: 0.128663\tAccuracy: 96.79%\n",
-      "4\tValidation loss: 0.086076\tBest loss: 0.086076\tAccuracy: 97.69%\n",
-      "5\tValidation loss: 0.115353\tBest loss: 0.086076\tAccuracy: 97.77%\n",
-      "6\tValidation loss: 0.239142\tBest loss: 0.086076\tAccuracy: 95.15%\n",
-      "7\tValidation loss: 0.088810\tBest loss: 0.086076\tAccuracy: 98.12%\n",
-      "8\tValidation loss: 0.108763\tBest loss: 0.086076\tAccuracy: 97.81%\n",
-      "9\tValidation loss: 0.300808\tBest loss: 0.086076\tAccuracy: 96.17%\n",
-      "10\tValidation loss: 0.179260\tBest loss: 0.086076\tAccuracy: 97.46%\n",
-      "11\tValidation loss: 0.125690\tBest loss: 0.086076\tAccuracy: 98.48%\n",
-      "12\tValidation loss: 0.738371\tBest loss: 0.086076\tAccuracy: 77.72%\n",
-      "13\tValidation loss: 1.894743\tBest loss: 0.086076\tAccuracy: 78.54%\n",
-      "14\tValidation loss: 0.415678\tBest loss: 0.086076\tAccuracy: 78.50%\n",
-      "15\tValidation loss: 0.537646\tBest loss: 0.086076\tAccuracy: 75.45%\n",
-      "16\tValidation loss: 1.009708\tBest loss: 0.086076\tAccuracy: 53.99%\n",
-      "17\tValidation loss: 1.228350\tBest loss: 0.086076\tAccuracy: 38.15%\n",
-      "18\tValidation loss: 1.510606\tBest loss: 0.086076\tAccuracy: 29.44%\n",
-      "19\tValidation loss: 1.632344\tBest loss: 0.086076\tAccuracy: 22.01%\n",
-      "20\tValidation loss: 1.628246\tBest loss: 0.086076\tAccuracy: 22.01%\n",
-      "21\tValidation loss: 1.626765\tBest loss: 0.086076\tAccuracy: 22.01%\n",
-      "22\tValidation loss: 1.651615\tBest loss: 0.086076\tAccuracy: 18.73%\n",
-      "23\tValidation loss: 1.663751\tBest loss: 0.086076\tAccuracy: 19.27%\n",
-      "24\tValidation loss: 1.675138\tBest loss: 0.086076\tAccuracy: 22.01%\n",
-      "25\tValidation loss: 1.743664\tBest loss: 0.086076\tAccuracy: 18.73%\n",
-      "Early stopping!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "DNNClassifier(activation=<function elu at 0x7fd9e8a620d0>,\n",
-       "       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7fd9d5e628c8>,\n",
-       "       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\n",
-       "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
-       "       random_state=42)"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dnn_clf = DNNClassifier(random_state=42)\n",
     "dnn_clf.fit(X_train1, y_train1, n_epochs=1000, X_valid=X_valid1, y_valid=y_valid1)"
@@ -4343,18 +3271,7 @@
    "cell_type": "code",
    "execution_count": 121,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.98054096127651291"
-      ]
-     },
-     "execution_count": 121,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "\n",
@@ -4373,250 +3290,7 @@
    "cell_type": "code",
    "execution_count": 122,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
-      "[CV] n_neurons=10, learning_rate=0.05, activation=<function elu at 0x7fd9e8a620d0>, batch_size=100 \n",
-      "0\tValidation loss: 0.132355\tBest loss: 0.132355\tAccuracy: 96.44%\n",
-      "1\tValidation loss: 0.126329\tBest loss: 0.126329\tAccuracy: 96.21%\n",
-      "2\tValidation loss: 0.138284\tBest loss: 0.126329\tAccuracy: 96.76%\n",
-      "3\tValidation loss: 0.142094\tBest loss: 0.126329\tAccuracy: 96.25%\n",
-      "4\tValidation loss: 0.128141\tBest loss: 0.126329\tAccuracy: 96.76%\n",
-      "5\tValidation loss: 0.119928\tBest loss: 0.119928\tAccuracy: 97.26%\n",
-      "6\tValidation loss: 0.137134\tBest loss: 0.119928\tAccuracy: 96.72%\n",
-      "7\tValidation loss: 0.156194\tBest loss: 0.119928\tAccuracy: 96.79%\n",
-      "8\tValidation loss: 0.283938\tBest loss: 0.119928\tAccuracy: 94.53%\n",
-      "9\tValidation loss: 1.104801\tBest loss: 0.119928\tAccuracy: 52.38%\n",
-      "10\tValidation loss: 0.966833\tBest loss: 0.119928\tAccuracy: 53.09%\n",
-      "11\tValidation loss: 0.854368\tBest loss: 0.119928\tAccuracy: 57.47%\n",
-      "12\tValidation loss: 1.857330\tBest loss: 0.119928\tAccuracy: 38.98%\n",
-      "13\tValidation loss: 1.642338\tBest loss: 0.119928\tAccuracy: 18.73%\n",
-      "14\tValidation loss: 1.612854\tBest loss: 0.119928\tAccuracy: 22.01%\n",
-      "15\tValidation loss: 1.617682\tBest loss: 0.119928\tAccuracy: 22.01%\n",
-      "16\tValidation loss: 1.616873\tBest loss: 0.119928\tAccuracy: 22.01%\n",
-      "17\tValidation loss: 1.618228\tBest loss: 0.119928\tAccuracy: 19.27%\n",
-      "18\tValidation loss: 1.619055\tBest loss: 0.119928\tAccuracy: 19.27%\n",
-      "19\tValidation loss: 1.643334\tBest loss: 0.119928\tAccuracy: 19.08%\n",
-      "20\tValidation loss: 1.621200\tBest loss: 0.119928\tAccuracy: 19.08%\n",
-      "21\tValidation loss: 1.629823\tBest loss: 0.119928\tAccuracy: 19.27%\n",
-      "22\tValidation loss: 1.624553\tBest loss: 0.119928\tAccuracy: 18.73%\n",
-      "23\tValidation loss: 1.610214\tBest loss: 0.119928\tAccuracy: 20.91%\n",
-      "24\tValidation loss: 1.621143\tBest loss: 0.119928\tAccuracy: 22.01%\n",
-      "25\tValidation loss: 1.623761\tBest loss: 0.119928\tAccuracy: 22.01%\n",
-      "26\tValidation loss: 1.641760\tBest loss: 0.119928\tAccuracy: 18.73%\n",
-      "Early stopping!\n",
-      "[CV]  n_neurons=10, learning_rate=0.05, activation=<function elu at 0x7fd9e8a620d0>, batch_size=100, total=   5.6s\n",
-      "[CV] n_neurons=10, learning_rate=0.05, activation=<function elu at 0x7fd9e8a620d0>, batch_size=100 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.6s remaining:    0.0s\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\tValidation loss: 0.153707\tBest loss: 0.153707\tAccuracy: 95.74%\n",
-      "1\tValidation loss: 0.120703\tBest loss: 0.120703\tAccuracy: 96.56%\n",
-      "2\tValidation loss: 0.164706\tBest loss: 0.120703\tAccuracy: 96.05%\n",
-      "3\tValidation loss: 0.177875\tBest loss: 0.120703\tAccuracy: 95.19%\n",
-      "4\tValidation loss: 0.171004\tBest loss: 0.120703\tAccuracy: 95.19%\n",
-      "5\tValidation loss: 0.114746\tBest loss: 0.114746\tAccuracy: 96.83%\n",
-      "6\tValidation loss: 0.109637\tBest loss: 0.109637\tAccuracy: 97.26%\n",
-      "7\tValidation loss: 0.261533\tBest loss: 0.109637\tAccuracy: 94.96%\n",
-      "8\tValidation loss: 0.316743\tBest loss: 0.109637\tAccuracy: 94.02%\n",
-      "9\tValidation loss: 0.486484\tBest loss: 0.109637\tAccuracy: 77.56%\n",
-      "10\tValidation loss: 4.635532\tBest loss: 0.109637\tAccuracy: 53.95%\n",
-      "11\tValidation loss: 1.172422\tBest loss: 0.109637\tAccuracy: 48.36%\n",
-      "12\tValidation loss: 1.029865\tBest loss: 0.109637\tAccuracy: 55.98%\n",
-      "13\tValidation loss: 1.298800\tBest loss: 0.109637\tAccuracy: 36.08%\n",
-      "14\tValidation loss: 1.141950\tBest loss: 0.109637\tAccuracy: 38.08%\n",
-      "15\tValidation loss: 1.132486\tBest loss: 0.109637\tAccuracy: 38.90%\n",
-      "16\tValidation loss: 1.078486\tBest loss: 0.109637\tAccuracy: 45.78%\n",
-      "17\tValidation loss: 1.128344\tBest loss: 0.109637\tAccuracy: 45.07%\n",
-      "18\tValidation loss: 1.336244\tBest loss: 0.109637\tAccuracy: 34.40%\n",
-      "19\tValidation loss: 1.199178\tBest loss: 0.109637\tAccuracy: 39.87%\n",
-      "20\tValidation loss: 1.175845\tBest loss: 0.109637\tAccuracy: 40.11%\n",
-      "21\tValidation loss: 1.200430\tBest loss: 0.109637\tAccuracy: 40.30%\n",
-      "22\tValidation loss: 1.390084\tBest loss: 0.109637\tAccuracy: 34.60%\n",
-      "23\tValidation loss: 1.268129\tBest loss: 0.109637\tAccuracy: 40.23%\n",
-      "24\tValidation loss: 1.192210\tBest loss: 0.109637\tAccuracy: 40.30%\n",
-      "25\tValidation loss: 1.190541\tBest loss: 0.109637\tAccuracy: 41.99%\n",
-      "26\tValidation loss: 1.227676\tBest loss: 0.109637\tAccuracy: 38.62%\n",
-      "27\tValidation loss: 1.187587\tBest loss: 0.109637\tAccuracy: 39.44%\n",
-      "Early stopping!\n",
-      "[CV]  n_neurons=10, learning_rate=0.05, activation=<function elu at 0x7fd9e8a620d0>, batch_size=100, total=   5.9s\n",
-      "[CV] n_neurons=10, learning_rate=0.05, activation=<function elu at 0x7fd9e8a620d0>, batch_size=100 \n",
-      "0\tValidation loss: 0.182619\tBest loss: 0.182619\tAccuracy: 94.29%\n",
-      "1\tValidation loss: 0.152706\tBest loss: 0.152706\tAccuracy: 95.97%\n",
-      "2\tValidation loss: 0.193820\tBest loss: 0.152706\tAccuracy: 93.82%\n",
-      "3\tValidation loss: 0.195413\tBest loss: 0.152706\tAccuracy: 95.54%\n",
-      "4\tValidation loss: 0.171277\tBest loss: 0.152706\tAccuracy: 95.19%\n",
-      "5\tValidation loss: 0.140087\tBest loss: 0.140087\tAccuracy: 95.70%\n",
-      "6\tValidation loss: 0.170798\tBest loss: 0.140087\tAccuracy: 95.00%\n",
-      "7\tValidation loss: 0.163649\tBest loss: 0.140087\tAccuracy: 96.29%\n",
-      "8\tValidation loss: 0.199048\tBest loss: 0.140087\tAccuracy: 96.09%\n",
-      "9\tValidation loss: 1.552870\tBest loss: 0.140087\tAccuracy: 52.15%\n",
-      "10\tValidation loss: 0.813273\tBest loss: 0.140087\tAccuracy: 60.40%\n",
-      "11\tValidation loss: 0.775555\tBest loss: 0.140087\tAccuracy: 60.67%\n",
-      "12\tValidation loss: 0.775275\tBest loss: 0.140087\tAccuracy: 59.77%\n",
-      "13\tValidation loss: 0.770521\tBest loss: 0.140087\tAccuracy: 59.30%\n",
-      "14\tValidation loss: 0.734035\tBest loss: 0.140087\tAccuracy: 59.85%\n",
-      "15\tValidation loss: 0.744980\tBest loss: 0.140087\tAccuracy: 59.66%\n",
-      "16\tValidation loss: 0.785848\tBest loss: 0.140087\tAccuracy: 59.66%\n",
-      "17\tValidation loss: 0.776138\tBest loss: 0.140087\tAccuracy: 59.42%\n",
-      "18\tValidation loss: 0.764496\tBest loss: 0.140087\tAccuracy: 59.46%\n",
-      "19\tValidation loss: 0.763633\tBest loss: 0.140087\tAccuracy: 59.54%\n",
-      "20\tValidation loss: 0.743879\tBest loss: 0.140087\tAccuracy: 60.75%\n",
-      "21\tValidation loss: 0.763295\tBest loss: 0.140087\tAccuracy: 60.36%\n",
-      "22\tValidation loss: 0.717175\tBest loss: 0.140087\tAccuracy: 60.63%\n",
-      "23\tValidation loss: 1.869954\tBest loss: 0.140087\tAccuracy: 29.28%\n",
-      "24\tValidation loss: 1.215518\tBest loss: 0.140087\tAccuracy: 38.86%\n",
-      "25\tValidation loss: 1.196626\tBest loss: 0.140087\tAccuracy: 38.62%\n",
-      "26\tValidation loss: 1.170714\tBest loss: 0.140087\tAccuracy: 42.38%\n",
-      "Early stopping!\n",
-      "[CV]  n_neurons=10, learning_rate=0.05, activation=<function elu at 0x7fd9e8a620d0>, batch_size=100, total=   6.9s\n",
-      "[CV] n_neurons=30, learning_rate=0.02, activation=<function relu at 0x7fd9e8a660d0>, batch_size=500 \n",
-      "0\tValidation loss: 0.171512\tBest loss: 0.171512\tAccuracy: 95.07%\n",
-      "1\tValidation loss: 0.095914\tBest loss: 0.095914\tAccuracy: 97.03%\n",
-      "2\tValidation loss: 0.099199\tBest loss: 0.095914\tAccuracy: 96.91%\n",
-      "3\tValidation loss: 0.093873\tBest loss: 0.093873\tAccuracy: 97.15%\n",
-      "4\tValidation loss: 0.073461\tBest loss: 0.073461\tAccuracy: 98.01%\n",
-      "5\tValidation loss: 0.084562\tBest loss: 0.073461\tAccuracy: 97.65%\n",
-      "6\tValidation loss: 0.071800\tBest loss: 0.071800\tAccuracy: 98.01%\n",
-      "7\tValidation loss: 0.088435\tBest loss: 0.071800\tAccuracy: 97.73%\n",
-      "8\tValidation loss: 0.082038\tBest loss: 0.071800\tAccuracy: 97.77%\n",
-      "9\tValidation loss: 0.080673\tBest loss: 0.071800\tAccuracy: 97.69%\n",
-      "10\tValidation loss: 0.081036\tBest loss: 0.071800\tAccuracy: 97.93%\n",
-      "11\tValidation loss: 0.092700\tBest loss: 0.071800\tAccuracy: 97.93%\n",
-      "12\tValidation loss: 0.081003\tBest loss: 0.071800\tAccuracy: 98.20%\n",
-      "13\tValidation loss: 0.075607\tBest loss: 0.071800\tAccuracy: 98.20%\n",
-      "14\tValidation loss: 0.092970\tBest loss: 0.071800\tAccuracy: 98.08%\n",
-      "15\tValidation loss: 0.108005\tBest loss: 0.071800\tAccuracy: 97.77%\n",
-      "16\tValidation loss: 0.082602\tBest loss: 0.071800\tAccuracy: 98.05%\n",
-      "17\tValidation loss: 0.114629\tBest loss: 0.071800\tAccuracy: 97.73%\n",
-      "18\tValidation loss: 0.099099\tBest loss: 0.071800\tAccuracy: 97.69%\n",
-      "19\tValidation loss: 0.075535\tBest loss: 0.071800\tAccuracy: 98.20%\n",
-      "20\tValidation loss: 0.102847\tBest loss: 0.071800\tAccuracy: 98.08%\n",
-      "21\tValidation loss: 0.089735\tBest loss: 0.071800\tAccuracy: 98.36%\n",
-      "22\tValidation loss: 0.080781\tBest loss: 0.071800\tAccuracy: 97.93%\n",
-      "23\tValidation loss: 0.073017\tBest loss: 0.071800\tAccuracy: 98.32%\n",
-      "24\tValidation loss: 0.091643\tBest loss: 0.071800\tAccuracy: 97.93%\n",
-      "25\tValidation loss: 0.113891\tBest loss: 0.071800\tAccuracy: 98.05%\n",
-      "26\tValidation loss: 0.094774\tBest loss: 0.071800\tAccuracy: 98.28%\n",
-      "27\tValidation loss: 0.086041\tBest loss: 0.071800\tAccuracy: 98.20%\n",
-      "Early stopping!\n",
-      "[CV]  n_neurons=30, learning_rate=0.02, activation=<function relu at 0x7fd9e8a660d0>, batch_size=500, total=   6.8s\n",
-      "[CV] n_neurons=30, learning_rate=0.02, activation=<function relu at 0x7fd9e8a660d0>, batch_size=500 \n",
-      "0\tValidation loss: 0.113188\tBest loss: 0.113188\tAccuracy: 96.60%\n",
-      "1\tValidation loss: 0.081384\tBest loss: 0.081384\tAccuracy: 97.58%\n",
-      "2\tValidation loss: 0.068770\tBest loss: 0.068770\tAccuracy: 98.12%\n",
-      "3\tValidation loss: 0.077316\tBest loss: 0.068770\tAccuracy: 97.73%\n",
-      "4\tValidation loss: 0.074333\tBest loss: 0.068770\tAccuracy: 97.97%\n",
-      "5\tValidation loss: 0.084735\tBest loss: 0.068770\tAccuracy: 97.30%\n",
-      "6\tValidation loss: 0.082893\tBest loss: 0.068770\tAccuracy: 97.69%\n",
-      "7\tValidation loss: 0.075860\tBest loss: 0.068770\tAccuracy: 97.65%\n",
-      "8\tValidation loss: 0.078686\tBest loss: 0.068770\tAccuracy: 97.77%\n",
-      "9\tValidation loss: 0.080869\tBest loss: 0.068770\tAccuracy: 97.77%\n",
-      "10\tValidation loss: 0.082026\tBest loss: 0.068770\tAccuracy: 98.12%\n",
-      "11\tValidation loss: 0.086516\tBest loss: 0.068770\tAccuracy: 97.69%\n",
-      "12\tValidation loss: 0.076660\tBest loss: 0.068770\tAccuracy: 98.12%\n",
-      "13\tValidation loss: 0.073815\tBest loss: 0.068770\tAccuracy: 98.08%\n",
-      "14\tValidation loss: 0.077873\tBest loss: 0.068770\tAccuracy: 98.20%\n",
-      "15\tValidation loss: 0.078704\tBest loss: 0.068770\tAccuracy: 97.93%\n",
-      "16\tValidation loss: 0.077061\tBest loss: 0.068770\tAccuracy: 98.28%\n",
-      "17\tValidation loss: 0.075423\tBest loss: 0.068770\tAccuracy: 97.93%\n",
-      "18\tValidation loss: 0.085646\tBest loss: 0.068770\tAccuracy: 98.24%\n",
-      "19\tValidation loss: 0.082202\tBest loss: 0.068770\tAccuracy: 98.05%\n",
-      "20\tValidation loss: 0.103338\tBest loss: 0.068770\tAccuracy: 97.46%\n",
-      "21\tValidation loss: 0.068182\tBest loss: 0.068182\tAccuracy: 98.40%\n",
-      "22\tValidation loss: 0.067592\tBest loss: 0.067592\tAccuracy: 97.93%\n",
-      "23\tValidation loss: 0.076756\tBest loss: 0.067592\tAccuracy: 98.28%\n",
-      "24\tValidation loss: 0.072327\tBest loss: 0.067592\tAccuracy: 98.48%\n",
-      "25\tValidation loss: 0.075613\tBest loss: 0.067592\tAccuracy: 98.44%\n",
-      "26\tValidation loss: 0.072291\tBest loss: 0.067592\tAccuracy: 98.40%\n",
-      "27\tValidation loss: 0.084550\tBest loss: 0.067592\tAccuracy: 98.28%\n",
-      "28\tValidation loss: 0.075566\tBest loss: 0.067592\tAccuracy: 98.36%\n",
-      "29\tValidation loss: 0.071688\tBest loss: 0.067592\tAccuracy: 98.28%\n",
-      "30\tValidation loss: 0.075556\tBest loss: 0.067592\tAccuracy: 98.24%\n",
-      "31\tValidation loss: 0.065671\tBest loss: 0.065671\tAccuracy: 98.40%\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "32\tValidation loss: 0.083471\tBest loss: 0.065671\tAccuracy: 98.40%\n",
-      "33\tValidation loss: 0.086415\tBest loss: 0.065671\tAccuracy: 98.59%\n",
-      "34\tValidation loss: 0.085613\tBest loss: 0.065671\tAccuracy: 98.36%\n",
-      "35\tValidation loss: 0.099534\tBest loss: 0.065671\tAccuracy: 98.28%\n",
-      "36\tValidation loss: 0.102709\tBest loss: 0.065671\tAccuracy: 98.32%\n",
-      "37\tValidation loss: 0.093125\tBest loss: 0.065671\tAccuracy: 98.20%\n",
-      "38\tValidation loss: 0.109501\tBest loss: 0.065671\tAccuracy: 97.85%\n",
-      "39\tValidation loss: 0.109443\tBest loss: 0.065671\tAccuracy: 98.44%\n",
-      "40\tValidation loss: 0.087260\tBest loss: 0.065671\tAccuracy: 98.36%\n",
-      "41\tValidation loss: 0.106365\tBest loss: 0.065671\tAccuracy: 98.36%\n",
-      "42\tValidation loss: 0.102789\tBest loss: 0.065671\tAccuracy: 98.05%\n",
-      "43\tValidation loss: 0.094281\tBest loss: 0.065671\tAccuracy: 98.48%\n",
-      "44\tValidation loss: 0.094514\tBest loss: 0.065671\tAccuracy: 98.40%\n",
-      "[...and much later...]\n",
-      "20\tValidation loss: 0.046808\tBest loss: 0.033867\tAccuracy: 98.83%\n",
-      "21\tValidation loss: 0.052966\tBest loss: 0.033867\tAccuracy: 98.91%\n",
-      "22\tValidation loss: 0.095892\tBest loss: 0.033867\tAccuracy: 98.08%\n",
-      "23\tValidation loss: 0.054250\tBest loss: 0.033867\tAccuracy: 98.87%\n",
-      "24\tValidation loss: 0.061026\tBest loss: 0.033867\tAccuracy: 98.87%\n",
-      "25\tValidation loss: 0.081977\tBest loss: 0.033867\tAccuracy: 98.67%\n",
-      "26\tValidation loss: 0.079819\tBest loss: 0.033867\tAccuracy: 98.71%\n",
-      "27\tValidation loss: 0.059824\tBest loss: 0.033867\tAccuracy: 98.75%\n",
-      "28\tValidation loss: 0.057758\tBest loss: 0.033867\tAccuracy: 98.94%\n",
-      "29\tValidation loss: 0.087165\tBest loss: 0.033867\tAccuracy: 98.91%\n",
-      "30\tValidation loss: 0.052274\tBest loss: 0.033867\tAccuracy: 99.10%\n",
-      "31\tValidation loss: 0.059831\tBest loss: 0.033867\tAccuracy: 98.79%\n",
-      "32\tValidation loss: 0.054240\tBest loss: 0.033867\tAccuracy: 98.91%\n",
-      "33\tValidation loss: 0.048165\tBest loss: 0.033867\tAccuracy: 98.94%\n",
-      "34\tValidation loss: 0.040565\tBest loss: 0.033867\tAccuracy: 99.18%\n",
-      "35\tValidation loss: 0.103207\tBest loss: 0.033867\tAccuracy: 98.28%\n",
-      "36\tValidation loss: 400.716797\tBest loss: 0.033867\tAccuracy: 71.46%\n",
-      "37\tValidation loss: 11.996887\tBest loss: 0.033867\tAccuracy: 96.09%\n",
-      "38\tValidation loss: 2.623182\tBest loss: 0.033867\tAccuracy: 96.56%\n",
-      "39\tValidation loss: 1.344962\tBest loss: 0.033867\tAccuracy: 97.69%\n",
-      "40\tValidation loss: 1.125381\tBest loss: 0.033867\tAccuracy: 97.42%\n",
-      "Early stopping!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "RandomizedSearchCV(cv=None, error_score='raise',\n",
-       "          estimator=DNNClassifier(activation=<function elu at 0x7fd9e8a620d0>,\n",
-       "       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7fd9d5e628c8>,\n",
-       "       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\n",
-       "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
-       "       random_state=42),\n",
-       "          fit_params={'y_valid': array([0, 4, ..., 1, 2], dtype=uint8), 'X_valid': array([[ 0.,  0., ...,  0.,  0.],\n",
-       "       [ 0.,  0., ...,  0.,  0.],\n",
-       "       ...,\n",
-       "       [ 0.,  0., ...,  0.,  0.],\n",
-       "       [ 0.,  0., ...,  0.,  0.]], dtype=float32), 'n_epochs': 1000},\n",
-       "          iid=True, n_iter=50, n_jobs=1,\n",
-       "          param_distributions={'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'activation': [<function relu at 0x7fd9e8a660d0>, <function elu at 0x7fd9e8a620d0>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7fd9db0b30d0>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7fd9d4ddca60>], 'batch_size': [10, 50, 100, 500]},\n",
-       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
-       "          return_train_score=True, scoring=None, verbose=2)"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import RandomizedSearchCV\n",
     "\n",
@@ -4635,38 +3309,24 @@
     "    #\"optimizer_class\": [tf.train.AdamOptimizer, partial(tf.train.MomentumOptimizer, momentum=0.95)],\n",
     "}\n",
     "\n",
-    "rnd_search = RandomizedSearchCV(DNNClassifier(random_state=42), param_distribs, n_iter=50,\n",    
+    "rnd_search = RandomizedSearchCV(DNNClassifier(random_state=42), param_distribs, n_iter=50,\n",
     "                                fit_params={\"X_valid\": X_valid1, \"y_valid\": y_valid1, \"n_epochs\": 1000},\n",
     "                                random_state=42, verbose=2)\n",
-    "rnd_search.fit(X_train1, y_train1)"
+    "rnd_search.fit(X_train1, y_train1)\n",
     "\n",
-    "# fit_params as a constructor argument was deprecated in [scikit-learn] version 0.19 and will be removed\n",
-    "# in version 0.21. Pass fit parameters to the fit method instead:"
-    "# rnd_search = RandomizedSearchCV(DNNClassifier(random_state=42), param_distribs, n_iter=50,\n",    
-    "#                                random_state=42, verbose=2)\n",
-    "# fit_params={\"X_valid\": X_valid1, \"y_valid\": y_valid1, \"n_epochs\": 1000}\n"
-    "# rnd_search.fit(X_train1, y_train1, **fit_params)\n",
+    "# fit_params as a constructor argument was deprecated in Scikit-Learn version 0.19 and will\n",
+    "# be removed in version 0.21. Pass fit parameters to the fit() method instead:\n",
+    "# rnd_search = RandomizedSearchCV(DNNClassifier(random_state=42), param_distribs, n_iter=50,\n",
+    "#                                 random_state=42, verbose=2)\n",
+    "# fit_params={\"X_valid\": X_valid1, \"y_valid\": y_valid1, \"n_epochs\": 1000}\n",
+    "# rnd_search.fit(X_train1, y_train1, **fit_params)\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 123,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'activation': <function __main__.leaky_relu.<locals>.parametrized_leaky_relu>,\n",
-       " 'batch_size': 500,\n",
-       " 'learning_rate': 0.01,\n",
-       " 'n_neurons': 140}"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "rnd_search.best_params_"
    ]
@@ -4675,18 +3335,7 @@
    "cell_type": "code",
    "execution_count": 124,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.99318933644677954"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_pred = rnd_search.predict(X_test1)\n",
     "accuracy_score(y_test1, y_pred)"
@@ -4742,71 +3391,7 @@
    "cell_type": "code",
    "execution_count": 126,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\tValidation loss: 0.090732\tBest loss: 0.090732\tAccuracy: 97.22%\n",
-      "1\tValidation loss: 0.052198\tBest loss: 0.052198\tAccuracy: 98.40%\n",
-      "2\tValidation loss: 0.040040\tBest loss: 0.040040\tAccuracy: 98.94%\n",
-      "3\tValidation loss: 0.057495\tBest loss: 0.040040\tAccuracy: 98.55%\n",
-      "4\tValidation loss: 0.045600\tBest loss: 0.040040\tAccuracy: 98.75%\n",
-      "5\tValidation loss: 0.062344\tBest loss: 0.040040\tAccuracy: 98.48%\n",
-      "6\tValidation loss: 0.048719\tBest loss: 0.040040\tAccuracy: 98.67%\n",
-      "7\tValidation loss: 0.050346\tBest loss: 0.040040\tAccuracy: 98.79%\n",
-      "8\tValidation loss: 0.051224\tBest loss: 0.040040\tAccuracy: 98.79%\n",
-      "9\tValidation loss: 0.036505\tBest loss: 0.036505\tAccuracy: 98.98%\n",
-      "10\tValidation loss: 0.052532\tBest loss: 0.036505\tAccuracy: 98.71%\n",
-      "11\tValidation loss: 0.057086\tBest loss: 0.036505\tAccuracy: 99.10%\n",
-      "12\tValidation loss: 0.036754\tBest loss: 0.036505\tAccuracy: 99.06%\n",
-      "13\tValidation loss: 0.046782\tBest loss: 0.036505\tAccuracy: 98.87%\n",
-      "14\tValidation loss: 0.048929\tBest loss: 0.036505\tAccuracy: 98.91%\n",
-      "15\tValidation loss: 0.052919\tBest loss: 0.036505\tAccuracy: 98.75%\n",
-      "16\tValidation loss: 0.054287\tBest loss: 0.036505\tAccuracy: 98.67%\n",
-      "17\tValidation loss: 0.047722\tBest loss: 0.036505\tAccuracy: 98.79%\n",
-      "18\tValidation loss: 0.040474\tBest loss: 0.036505\tAccuracy: 99.14%\n",
-      "19\tValidation loss: 0.033867\tBest loss: 0.033867\tAccuracy: 99.14%\n",
-      "20\tValidation loss: 0.046808\tBest loss: 0.033867\tAccuracy: 98.83%\n",
-      "21\tValidation loss: 0.052966\tBest loss: 0.033867\tAccuracy: 98.91%\n",
-      "22\tValidation loss: 0.095892\tBest loss: 0.033867\tAccuracy: 98.08%\n",
-      "23\tValidation loss: 0.054250\tBest loss: 0.033867\tAccuracy: 98.87%\n",
-      "24\tValidation loss: 0.061026\tBest loss: 0.033867\tAccuracy: 98.87%\n",
-      "25\tValidation loss: 0.081977\tBest loss: 0.033867\tAccuracy: 98.67%\n",
-      "26\tValidation loss: 0.079819\tBest loss: 0.033867\tAccuracy: 98.71%\n",
-      "27\tValidation loss: 0.059824\tBest loss: 0.033867\tAccuracy: 98.75%\n",
-      "28\tValidation loss: 0.057758\tBest loss: 0.033867\tAccuracy: 98.94%\n",
-      "29\tValidation loss: 0.087165\tBest loss: 0.033867\tAccuracy: 98.91%\n",
-      "30\tValidation loss: 0.052274\tBest loss: 0.033867\tAccuracy: 99.10%\n",
-      "31\tValidation loss: 0.059831\tBest loss: 0.033867\tAccuracy: 98.79%\n",
-      "32\tValidation loss: 0.054240\tBest loss: 0.033867\tAccuracy: 98.91%\n",
-      "33\tValidation loss: 0.048165\tBest loss: 0.033867\tAccuracy: 98.94%\n",
-      "34\tValidation loss: 0.040565\tBest loss: 0.033867\tAccuracy: 99.18%\n",
-      "35\tValidation loss: 0.103207\tBest loss: 0.033867\tAccuracy: 98.28%\n",
-      "36\tValidation loss: 400.716797\tBest loss: 0.033867\tAccuracy: 71.46%\n",
-      "37\tValidation loss: 11.996887\tBest loss: 0.033867\tAccuracy: 96.09%\n",
-      "38\tValidation loss: 2.623182\tBest loss: 0.033867\tAccuracy: 96.56%\n",
-      "39\tValidation loss: 1.344962\tBest loss: 0.033867\tAccuracy: 97.69%\n",
-      "40\tValidation loss: 1.125381\tBest loss: 0.033867\tAccuracy: 97.42%\n",
-      "Early stopping!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7fd9d19e37b8>,\n",
-       "       batch_norm_momentum=None, batch_size=500, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7fd9d5e628c8>,\n",
-       "       learning_rate=0.01, n_hidden_layers=5, n_neurons=140,\n",
-       "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
-       "       random_state=42)"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dnn_clf = DNNClassifier(activation=leaky_relu(alpha=0.1), batch_size=500, learning_rate=0.01,\n",
     "                        n_neurons=140, random_state=42)\n",
@@ -4831,18 +3416,7 @@
    "cell_type": "code",
    "execution_count": 127,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.99318933644677954"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_pred = dnn_clf.predict(X_test1)\n",
     "accuracy_score(y_test1, y_pred)"
@@ -4859,100 +3433,7 @@
    "cell_type": "code",
    "execution_count": 128,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\tValidation loss: 0.046053\tBest loss: 0.046053\tAccuracy: 98.67%\n",
-      "1\tValidation loss: 0.032228\tBest loss: 0.032228\tAccuracy: 98.83%\n",
-      "2\tValidation loss: 0.032974\tBest loss: 0.032228\tAccuracy: 98.83%\n",
-      "3\tValidation loss: 0.035961\tBest loss: 0.032228\tAccuracy: 98.94%\n",
-      "4\tValidation loss: 0.040250\tBest loss: 0.032228\tAccuracy: 98.94%\n",
-      "5\tValidation loss: 0.033051\tBest loss: 0.032228\tAccuracy: 99.06%\n",
-      "6\tValidation loss: 0.056053\tBest loss: 0.032228\tAccuracy: 98.32%\n",
-      "7\tValidation loss: 0.031729\tBest loss: 0.031729\tAccuracy: 99.18%\n",
-      "8\tValidation loss: 0.027662\tBest loss: 0.027662\tAccuracy: 99.26%\n",
-      "9\tValidation loss: 0.034074\tBest loss: 0.027662\tAccuracy: 98.94%\n",
-      "10\tValidation loss: 0.032173\tBest loss: 0.027662\tAccuracy: 99.06%\n",
-      "11\tValidation loss: 0.030538\tBest loss: 0.027662\tAccuracy: 99.10%\n",
-      "12\tValidation loss: 0.030337\tBest loss: 0.027662\tAccuracy: 99.10%\n",
-      "13\tValidation loss: 0.022219\tBest loss: 0.022219\tAccuracy: 99.45%\n",
-      "14\tValidation loss: 0.036824\tBest loss: 0.022219\tAccuracy: 99.14%\n",
-      "15\tValidation loss: 0.033945\tBest loss: 0.022219\tAccuracy: 99.18%\n",
-      "16\tValidation loss: 0.032533\tBest loss: 0.022219\tAccuracy: 98.98%\n",
-      "17\tValidation loss: 0.037204\tBest loss: 0.022219\tAccuracy: 99.02%\n",
-      "18\tValidation loss: 0.026982\tBest loss: 0.022219\tAccuracy: 99.34%\n",
-      "19\tValidation loss: 0.022094\tBest loss: 0.022094\tAccuracy: 99.53%\n",
-      "20\tValidation loss: 0.026196\tBest loss: 0.022094\tAccuracy: 99.26%\n",
-      "21\tValidation loss: 0.022107\tBest loss: 0.022094\tAccuracy: 99.49%\n",
-      "22\tValidation loss: 0.021436\tBest loss: 0.021436\tAccuracy: 99.53%\n",
-      "23\tValidation loss: 0.025607\tBest loss: 0.021436\tAccuracy: 99.37%\n",
-      "24\tValidation loss: 0.038882\tBest loss: 0.021436\tAccuracy: 99.22%\n",
-      "25\tValidation loss: 0.032011\tBest loss: 0.021436\tAccuracy: 99.26%\n",
-      "26\tValidation loss: 0.027673\tBest loss: 0.021436\tAccuracy: 99.22%\n",
-      "27\tValidation loss: 0.026874\tBest loss: 0.021436\tAccuracy: 99.30%\n",
-      "28\tValidation loss: 0.021123\tBest loss: 0.021123\tAccuracy: 99.41%\n",
-      "29\tValidation loss: 0.024784\tBest loss: 0.021123\tAccuracy: 99.45%\n",
-      "30\tValidation loss: 0.024108\tBest loss: 0.021123\tAccuracy: 99.49%\n",
-      "31\tValidation loss: 0.028439\tBest loss: 0.021123\tAccuracy: 99.37%\n",
-      "32\tValidation loss: 0.032366\tBest loss: 0.021123\tAccuracy: 99.22%\n",
-      "33\tValidation loss: 0.037057\tBest loss: 0.021123\tAccuracy: 99.18%\n",
-      "34\tValidation loss: 0.042305\tBest loss: 0.021123\tAccuracy: 98.98%\n",
-      "35\tValidation loss: 0.039662\tBest loss: 0.021123\tAccuracy: 99.14%\n",
-      "36\tValidation loss: 0.036299\tBest loss: 0.021123\tAccuracy: 99.14%\n",
-      "37\tValidation loss: 0.026997\tBest loss: 0.021123\tAccuracy: 99.53%\n",
-      "38\tValidation loss: 0.034407\tBest loss: 0.021123\tAccuracy: 99.22%\n",
-      "39\tValidation loss: 0.027668\tBest loss: 0.021123\tAccuracy: 99.41%\n",
-      "40\tValidation loss: 0.029128\tBest loss: 0.021123\tAccuracy: 99.30%\n",
-      "41\tValidation loss: 0.033564\tBest loss: 0.021123\tAccuracy: 99.14%\n",
-      "42\tValidation loss: 0.033810\tBest loss: 0.021123\tAccuracy: 99.30%\n",
-      "43\tValidation loss: 0.044953\tBest loss: 0.021123\tAccuracy: 98.98%\n",
-      "44\tValidation loss: 0.026280\tBest loss: 0.021123\tAccuracy: 99.26%\n",
-      "45\tValidation loss: 0.020275\tBest loss: 0.020275\tAccuracy: 99.61%\n",
-      "46\tValidation loss: 0.018810\tBest loss: 0.018810\tAccuracy: 99.45%\n",
-      "47\tValidation loss: 0.027529\tBest loss: 0.018810\tAccuracy: 99.18%\n",
-      "48\tValidation loss: 0.018120\tBest loss: 0.018120\tAccuracy: 99.53%\n",
-      "49\tValidation loss: 0.019378\tBest loss: 0.018120\tAccuracy: 99.45%\n",
-      "50\tValidation loss: 0.029760\tBest loss: 0.018120\tAccuracy: 99.34%\n",
-      "51\tValidation loss: 0.035702\tBest loss: 0.018120\tAccuracy: 99.26%\n",
-      "52\tValidation loss: 0.032662\tBest loss: 0.018120\tAccuracy: 99.02%\n",
-      "53\tValidation loss: 0.026943\tBest loss: 0.018120\tAccuracy: 99.37%\n",
-      "54\tValidation loss: 0.029007\tBest loss: 0.018120\tAccuracy: 99.53%\n",
-      "55\tValidation loss: 0.021956\tBest loss: 0.018120\tAccuracy: 99.49%\n",
-      "56\tValidation loss: 0.018983\tBest loss: 0.018120\tAccuracy: 99.61%\n",
-      "57\tValidation loss: 0.022788\tBest loss: 0.018120\tAccuracy: 99.49%\n",
-      "58\tValidation loss: 0.019578\tBest loss: 0.018120\tAccuracy: 99.61%\n",
-      "59\tValidation loss: 0.021676\tBest loss: 0.018120\tAccuracy: 99.61%\n",
-      "60\tValidation loss: 0.021580\tBest loss: 0.018120\tAccuracy: 99.65%\n",
-      "61\tValidation loss: 0.021467\tBest loss: 0.018120\tAccuracy: 99.65%\n",
-      "62\tValidation loss: 0.020513\tBest loss: 0.018120\tAccuracy: 99.65%\n",
-      "63\tValidation loss: 0.020252\tBest loss: 0.018120\tAccuracy: 99.65%\n",
-      "64\tValidation loss: 0.021724\tBest loss: 0.018120\tAccuracy: 99.65%\n",
-      "65\tValidation loss: 0.021499\tBest loss: 0.018120\tAccuracy: 99.69%\n",
-      "66\tValidation loss: 0.021627\tBest loss: 0.018120\tAccuracy: 99.69%\n",
-      "67\tValidation loss: 0.021569\tBest loss: 0.018120\tAccuracy: 99.69%\n",
-      "68\tValidation loss: 0.021727\tBest loss: 0.018120\tAccuracy: 99.69%\n",
-      "69\tValidation loss: 0.021104\tBest loss: 0.018120\tAccuracy: 99.69%\n",
-      "Early stopping!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7fd9d19e3c80>,\n",
-       "       batch_norm_momentum=0.95, batch_size=500, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7fd9d5e628c8>,\n",
-       "       learning_rate=0.01, n_hidden_layers=5, n_neurons=90,\n",
-       "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
-       "       random_state=42)"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dnn_clf_bn = DNNClassifier(activation=leaky_relu(alpha=0.1), batch_size=500, learning_rate=0.01,\n",
     "                           n_neurons=90, random_state=42,\n",
@@ -4971,18 +3452,7 @@
    "cell_type": "code",
    "execution_count": 129,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.99241097489784003"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_pred = dnn_clf_bn.predict(X_test1)\n",
     "accuracy_score(y_test1, y_pred)"
@@ -4999,222 +3469,7 @@
    "cell_type": "code",
    "execution_count": 130,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
-      "[CV] activation=<function relu at 0x7fd9e8a660d0>, n_neurons=70, learning_rate=0.01, batch_norm_momentum=0.99, batch_size=50 \n",
-      "0\tValidation loss: 0.113224\tBest loss: 0.113224\tAccuracy: 97.30%\n",
-      "1\tValidation loss: 0.064190\tBest loss: 0.064190\tAccuracy: 98.24%\n",
-      "2\tValidation loss: 0.080173\tBest loss: 0.064190\tAccuracy: 98.28%\n",
-      "3\tValidation loss: 0.059603\tBest loss: 0.059603\tAccuracy: 98.28%\n",
-      "4\tValidation loss: 0.043533\tBest loss: 0.043533\tAccuracy: 98.48%\n",
-      "5\tValidation loss: 0.040107\tBest loss: 0.040107\tAccuracy: 98.87%\n",
-      "6\tValidation loss: 0.051212\tBest loss: 0.040107\tAccuracy: 98.24%\n",
-      "7\tValidation loss: 0.046029\tBest loss: 0.040107\tAccuracy: 98.71%\n",
-      "8\tValidation loss: 0.053079\tBest loss: 0.040107\tAccuracy: 98.59%\n",
-      "9\tValidation loss: 0.066891\tBest loss: 0.040107\tAccuracy: 98.28%\n",
-      "10\tValidation loss: 0.037712\tBest loss: 0.037712\tAccuracy: 98.83%\n",
-      "11\tValidation loss: 0.055569\tBest loss: 0.037712\tAccuracy: 98.55%\n",
-      "12\tValidation loss: 0.040949\tBest loss: 0.037712\tAccuracy: 98.98%\n",
-      "13\tValidation loss: 0.077433\tBest loss: 0.037712\tAccuracy: 98.36%\n",
-      "14\tValidation loss: 0.065955\tBest loss: 0.037712\tAccuracy: 98.63%\n",
-      "15\tValidation loss: 0.038968\tBest loss: 0.037712\tAccuracy: 99.02%\n",
-      "16\tValidation loss: 0.039190\tBest loss: 0.037712\tAccuracy: 99.06%\n",
-      "17\tValidation loss: 0.050690\tBest loss: 0.037712\tAccuracy: 98.71%\n",
-      "18\tValidation loss: 0.043054\tBest loss: 0.037712\tAccuracy: 99.02%\n",
-      "19\tValidation loss: 0.063156\tBest loss: 0.037712\tAccuracy: 98.71%\n",
-      "20\tValidation loss: 0.043066\tBest loss: 0.037712\tAccuracy: 99.14%\n",
-      "21\tValidation loss: 0.058145\tBest loss: 0.037712\tAccuracy: 98.79%\n",
-      "22\tValidation loss: 0.039590\tBest loss: 0.037712\tAccuracy: 99.06%\n",
-      "23\tValidation loss: 0.049981\tBest loss: 0.037712\tAccuracy: 98.75%\n",
-      "24\tValidation loss: 0.047458\tBest loss: 0.037712\tAccuracy: 99.10%\n",
-      "25\tValidation loss: 0.040638\tBest loss: 0.037712\tAccuracy: 99.06%\n",
-      "26\tValidation loss: 0.041426\tBest loss: 0.037712\tAccuracy: 98.98%\n",
-      "27\tValidation loss: 0.041325\tBest loss: 0.037712\tAccuracy: 98.98%\n",
-      "28\tValidation loss: 0.054609\tBest loss: 0.037712\tAccuracy: 98.91%\n",
-      "29\tValidation loss: 0.067671\tBest loss: 0.037712\tAccuracy: 98.75%\n",
-      "30\tValidation loss: 0.037608\tBest loss: 0.037608\tAccuracy: 98.79%\n",
-      "31\tValidation loss: 0.047441\tBest loss: 0.037608\tAccuracy: 98.98%\n",
-      "32\tValidation loss: 0.053716\tBest loss: 0.037608\tAccuracy: 99.02%\n",
-      "33\tValidation loss: 0.045445\tBest loss: 0.037608\tAccuracy: 98.83%\n",
-      "34\tValidation loss: 0.046023\tBest loss: 0.037608\tAccuracy: 98.94%\n",
-      "35\tValidation loss: 0.050073\tBest loss: 0.037608\tAccuracy: 98.91%\n",
-      "36\tValidation loss: 0.051887\tBest loss: 0.037608\tAccuracy: 98.87%\n",
-      "37\tValidation loss: 0.050272\tBest loss: 0.037608\tAccuracy: 99.02%\n",
-      "38\tValidation loss: 0.043531\tBest loss: 0.037608\tAccuracy: 99.10%\n",
-      "39\tValidation loss: 0.054661\tBest loss: 0.037608\tAccuracy: 98.87%\n",
-      "40\tValidation loss: 0.047607\tBest loss: 0.037608\tAccuracy: 98.87%\n",
-      "41\tValidation loss: 0.051862\tBest loss: 0.037608\tAccuracy: 99.14%\n",
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-      "48\tValidation loss: 0.027679\tBest loss: 0.027679\tAccuracy: 99.10%\n",
-      "49\tValidation loss: 0.033783\tBest loss: 0.027679\tAccuracy: 98.94%\n",
-      "50\tValidation loss: 0.032935\tBest loss: 0.027679\tAccuracy: 99.41%\n",
-      "51\tValidation loss: 0.042930\tBest loss: 0.027679\tAccuracy: 98.98%\n",
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-      "53\tValidation loss: 0.047336\tBest loss: 0.027679\tAccuracy: 98.91%\n",
-      "54\tValidation loss: 0.036523\tBest loss: 0.027679\tAccuracy: 99.14%\n",
-      "55\tValidation loss: 0.064401\tBest loss: 0.027679\tAccuracy: 98.94%\n",
-      "56\tValidation loss: 0.047686\tBest loss: 0.027679\tAccuracy: 98.83%\n",
-      "57\tValidation loss: 0.049083\tBest loss: 0.027679\tAccuracy: 98.98%\n",
-      "58\tValidation loss: 0.057310\tBest loss: 0.027679\tAccuracy: 99.10%\n",
-      "59\tValidation loss: 0.043757\tBest loss: 0.027679\tAccuracy: 99.14%\n",
-      "60\tValidation loss: 0.058742\tBest loss: 0.027679\tAccuracy: 99.02%\n",
-      "61\tValidation loss: 0.055049\tBest loss: 0.027679\tAccuracy: 99.06%\n",
-      "62\tValidation loss: 0.039837\tBest loss: 0.027679\tAccuracy: 99.18%\n",
-      "63\tValidation loss: 0.057108\tBest loss: 0.027679\tAccuracy: 99.06%\n",
-      "64\tValidation loss: 0.043212\tBest loss: 0.027679\tAccuracy: 98.98%\n",
-      "65\tValidation loss: 0.046874\tBest loss: 0.027679\tAccuracy: 99.18%\n",
-      "66\tValidation loss: 0.052819\tBest loss: 0.027679\tAccuracy: 99.10%\n",
-      "67\tValidation loss: 0.045977\tBest loss: 0.027679\tAccuracy: 99.14%\n",
-      "68\tValidation loss: 0.053290\tBest loss: 0.027679\tAccuracy: 99.10%\n",
-      "69\tValidation loss: 0.052941\tBest loss: 0.027679\tAccuracy: 99.06%\n",
-      "Early stopping!\n",
-      "[CV]  activation=<function relu at 0x7fd9e8a660d0>, n_neurons=70, learning_rate=0.01, batch_norm_momentum=0.99, batch_size=50, total= 2.7min\n",
-      "[CV] activation=<function relu at 0x7fd9e8a660d0>, n_neurons=70, learning_rate=0.01, batch_norm_momentum=0.99, batch_size=50 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.7min remaining:    0.0s\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\tValidation loss: 0.144984\tBest loss: 0.144984\tAccuracy: 96.40%\n",
-      "1\tValidation loss: 0.067873\tBest loss: 0.067873\tAccuracy: 98.44%\n",
-      "2\tValidation loss: 0.091854\tBest loss: 0.067873\tAccuracy: 97.30%\n",
-      "3\tValidation loss: 0.074647\tBest loss: 0.067873\tAccuracy: 98.05%\n",
-      "4\tValidation loss: 0.053722\tBest loss: 0.053722\tAccuracy: 98.48%\n",
-      "5\tValidation loss: 0.049216\tBest loss: 0.049216\tAccuracy: 98.44%\n",
-      "6\tValidation loss: 0.057619\tBest loss: 0.049216\tAccuracy: 98.48%\n",
-      "7\tValidation loss: 0.045842\tBest loss: 0.045842\tAccuracy: 98.75%\n",
-      "8\tValidation loss: 0.042398\tBest loss: 0.042398\tAccuracy: 98.63%\n",
-      "9\tValidation loss: 0.052629\tBest loss: 0.042398\tAccuracy: 98.63%\n",
-      "10\tValidation loss: 0.056892\tBest loss: 0.042398\tAccuracy: 98.63%\n",
-      "11\tValidation loss: 0.051838\tBest loss: 0.042398\tAccuracy: 98.75%\n",
-      "12\tValidation loss: 0.042647\tBest loss: 0.042398\tAccuracy: 98.67%\n",
-      "13\tValidation loss: 0.061297\tBest loss: 0.042398\tAccuracy: 98.59%\n",
-      "14\tValidation loss: 0.049706\tBest loss: 0.042398\tAccuracy: 98.87%\n",
-      "15\tValidation loss: 0.061934\tBest loss: 0.042398\tAccuracy: 98.79%\n",
-      "16\tValidation loss: 0.049027\tBest loss: 0.042398\tAccuracy: 98.87%\n",
-      "17\tValidation loss: 0.052187\tBest loss: 0.042398\tAccuracy: 98.79%\n",
-      "18\tValidation loss: 0.052031\tBest loss: 0.042398\tAccuracy: 98.94%\n",
-      "[...and much later...]\n",
-      "13\tValidation loss: 0.043686\tBest loss: 0.040332\tAccuracy: 99.02%\n",
-      "14\tValidation loss: 0.046940\tBest loss: 0.040332\tAccuracy: 99.18%\n",
-      "15\tValidation loss: 0.045355\tBest loss: 0.040332\tAccuracy: 99.14%\n",
-      "16\tValidation loss: 0.084697\tBest loss: 0.040332\tAccuracy: 98.87%\n",
-      "17\tValidation loss: 0.123538\tBest loss: 0.040332\tAccuracy: 97.81%\n",
-      "18\tValidation loss: 0.296928\tBest loss: 0.040332\tAccuracy: 97.50%\n",
-      "19\tValidation loss: 0.053660\tBest loss: 0.040332\tAccuracy: 98.91%\n",
-      "20\tValidation loss: 0.045684\tBest loss: 0.040332\tAccuracy: 98.94%\n",
-      "21\tValidation loss: 0.051971\tBest loss: 0.040332\tAccuracy: 99.14%\n",
-      "22\tValidation loss: 0.071830\tBest loss: 0.040332\tAccuracy: 99.06%\n",
-      "23\tValidation loss: 0.069619\tBest loss: 0.040332\tAccuracy: 98.79%\n",
-      "24\tValidation loss: 0.086642\tBest loss: 0.040332\tAccuracy: 98.71%\n",
-      "25\tValidation loss: 0.072563\tBest loss: 0.040332\tAccuracy: 98.83%\n",
-      "26\tValidation loss: 0.058974\tBest loss: 0.040332\tAccuracy: 99.06%\n",
-      "27\tValidation loss: 0.048388\tBest loss: 0.040332\tAccuracy: 98.98%\n",
-      "28\tValidation loss: 0.054847\tBest loss: 0.040332\tAccuracy: 99.06%\n",
-      "29\tValidation loss: 0.077242\tBest loss: 0.040332\tAccuracy: 98.91%\n",
-      "30\tValidation loss: 0.556978\tBest loss: 0.040332\tAccuracy: 95.54%\n",
-      "Early stopping!\n",
-      "[CV]  activation=<function elu at 0x7fd9e8a620d0>, n_neurons=140, learning_rate=0.05, batch_norm_momentum=0.99, batch_size=50, total= 1.9min\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 355.8min finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\tValidation loss: 0.076371\tBest loss: 0.076371\tAccuracy: 97.85%\n",
-      "1\tValidation loss: 0.049312\tBest loss: 0.049312\tAccuracy: 98.63%\n",
-      "2\tValidation loss: 0.033071\tBest loss: 0.033071\tAccuracy: 98.94%\n",
-      "3\tValidation loss: 0.027357\tBest loss: 0.027357\tAccuracy: 99.10%\n",
-      "4\tValidation loss: 0.028748\tBest loss: 0.027357\tAccuracy: 99.26%\n",
-      "5\tValidation loss: 0.036602\tBest loss: 0.027357\tAccuracy: 98.94%\n",
-      "6\tValidation loss: 0.048089\tBest loss: 0.027357\tAccuracy: 98.94%\n",
-      "7\tValidation loss: 0.030332\tBest loss: 0.027357\tAccuracy: 99.30%\n",
-      "8\tValidation loss: 0.029336\tBest loss: 0.027357\tAccuracy: 99.22%\n",
-      "9\tValidation loss: 0.033328\tBest loss: 0.027357\tAccuracy: 99.26%\n",
-      "10\tValidation loss: 0.041745\tBest loss: 0.027357\tAccuracy: 98.98%\n",
-      "11\tValidation loss: 0.048739\tBest loss: 0.027357\tAccuracy: 98.75%\n",
-      "12\tValidation loss: 0.049520\tBest loss: 0.027357\tAccuracy: 98.94%\n",
-      "13\tValidation loss: 0.034222\tBest loss: 0.027357\tAccuracy: 99.18%\n",
-      "14\tValidation loss: 0.040270\tBest loss: 0.027357\tAccuracy: 99.34%\n",
-      "15\tValidation loss: 0.033074\tBest loss: 0.027357\tAccuracy: 99.37%\n",
-      "16\tValidation loss: 0.035130\tBest loss: 0.027357\tAccuracy: 99.06%\n",
-      "17\tValidation loss: 0.031875\tBest loss: 0.027357\tAccuracy: 99.18%\n",
-      "18\tValidation loss: 0.034898\tBest loss: 0.027357\tAccuracy: 99.37%\n",
-      "19\tValidation loss: 0.019222\tBest loss: 0.019222\tAccuracy: 99.53%\n",
-      "20\tValidation loss: 0.043814\tBest loss: 0.019222\tAccuracy: 99.37%\n",
-      "21\tValidation loss: 0.028773\tBest loss: 0.019222\tAccuracy: 99.34%\n",
-      "22\tValidation loss: 0.024850\tBest loss: 0.019222\tAccuracy: 99.45%\n",
-      "23\tValidation loss: 0.021789\tBest loss: 0.019222\tAccuracy: 99.45%\n",
-      "24\tValidation loss: 0.028846\tBest loss: 0.019222\tAccuracy: 99.37%\n",
-      "25\tValidation loss: 0.064211\tBest loss: 0.019222\tAccuracy: 98.98%\n",
-      "26\tValidation loss: 0.024425\tBest loss: 0.019222\tAccuracy: 99.49%\n",
-      "27\tValidation loss: 0.035453\tBest loss: 0.019222\tAccuracy: 99.22%\n",
-      "28\tValidation loss: 0.023940\tBest loss: 0.019222\tAccuracy: 99.37%\n",
-      "29\tValidation loss: 0.041495\tBest loss: 0.019222\tAccuracy: 99.18%\n",
-      "30\tValidation loss: 0.028030\tBest loss: 0.019222\tAccuracy: 99.37%\n",
-      "31\tValidation loss: 0.028003\tBest loss: 0.019222\tAccuracy: 99.49%\n",
-      "32\tValidation loss: 0.026579\tBest loss: 0.019222\tAccuracy: 99.45%\n",
-      "33\tValidation loss: 0.037838\tBest loss: 0.019222\tAccuracy: 98.91%\n",
-      "34\tValidation loss: 0.026082\tBest loss: 0.019222\tAccuracy: 99.49%\n",
-      "35\tValidation loss: 0.031529\tBest loss: 0.019222\tAccuracy: 99.34%\n",
-      "36\tValidation loss: 0.028220\tBest loss: 0.019222\tAccuracy: 99.18%\n",
-      "37\tValidation loss: 0.038546\tBest loss: 0.019222\tAccuracy: 99.10%\n",
-      "38\tValidation loss: 0.041586\tBest loss: 0.019222\tAccuracy: 98.75%\n",
-      "39\tValidation loss: 0.038835\tBest loss: 0.019222\tAccuracy: 99.41%\n",
-      "40\tValidation loss: 0.042555\tBest loss: 0.019222\tAccuracy: 99.14%\n",
-      "Early stopping!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "RandomizedSearchCV(cv=None, error_score='raise',\n",
-       "          estimator=DNNClassifier(activation=<function elu at 0x7fd9e8a620d0>,\n",
-       "       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7fd9d5e628c8>,\n",
-       "       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\n",
-       "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
-       "       random_state=42),\n",
-       "          fit_params={'y_valid': array([0, 4, ..., 1, 2], dtype=uint8), 'X_valid': array([[ 0.,  0., ...,  0.,  0.],\n",
-       "       [ 0.,  0., ...,  0.,  0.],\n",
-       "       ...,\n",
-       "       [ 0.,  0., ...,  0.,  0.],\n",
-       "       [ 0.,  0., ...,  0.,  0.]], dtype=float32), 'n_epochs': 1000},\n",
-       "          iid=True, n_iter=50, n_jobs=1,\n",
-       "          param_distributions={'batch_norm_momentum': [0.9, 0.95, 0.98, 0.99, 0.999], 'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'activation': [<function relu at 0x7fd9e8a660d0>, <function elu at 0x7fd9e8a620d0>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7fd9d19e3bf8>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7fd9d19e3a60>], 'batch_size': [10, 50, 100, 500]},\n",
-       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
-       "          return_train_score=True, scoring=None, verbose=2)"
-      ]
-     },
-     "execution_count": 130,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import RandomizedSearchCV\n",
     "\n",
@@ -5239,22 +3494,7 @@
    "cell_type": "code",
    "execution_count": 131,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'activation': <function tensorflow.python.ops.gen_nn_ops.relu>,\n",
-       " 'batch_norm_momentum': 0.98,\n",
-       " 'batch_size': 100,\n",
-       " 'learning_rate': 0.01,\n",
-       " 'n_neurons': 160}"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "rnd_search_bn.best_params_"
    ]
@@ -5263,18 +3503,7 @@
    "cell_type": "code",
    "execution_count": 132,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.99396769799571905"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_pred = rnd_search_bn.predict(X_test1)\n",
     "accuracy_score(y_test1, y_pred)"
@@ -5312,18 +3541,7 @@
    "cell_type": "code",
    "execution_count": 133,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.99914401883158566"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_pred = dnn_clf.predict(X_train1)\n",
     "accuracy_score(y_train1, y_pred)"
@@ -5340,75 +3558,7 @@
    "cell_type": "code",
    "execution_count": 134,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\tValidation loss: 0.162759\tBest loss: 0.162759\tAccuracy: 95.15%\n",
-      "1\tValidation loss: 0.120510\tBest loss: 0.120510\tAccuracy: 96.64%\n",
-      "2\tValidation loss: 0.110715\tBest loss: 0.110715\tAccuracy: 96.91%\n",
-      "3\tValidation loss: 0.104193\tBest loss: 0.104193\tAccuracy: 97.22%\n",
-      "4\tValidation loss: 0.103560\tBest loss: 0.103560\tAccuracy: 97.81%\n",
-      "5\tValidation loss: 0.087045\tBest loss: 0.087045\tAccuracy: 97.89%\n",
-      "6\tValidation loss: 0.087227\tBest loss: 0.087045\tAccuracy: 97.65%\n",
-      "7\tValidation loss: 0.079840\tBest loss: 0.079840\tAccuracy: 98.16%\n",
-      "8\tValidation loss: 0.083102\tBest loss: 0.079840\tAccuracy: 97.50%\n",
-      "9\tValidation loss: 0.076794\tBest loss: 0.076794\tAccuracy: 98.01%\n",
-      "10\tValidation loss: 0.074914\tBest loss: 0.074914\tAccuracy: 97.93%\n",
-      "11\tValidation loss: 0.073794\tBest loss: 0.073794\tAccuracy: 98.12%\n",
-      "12\tValidation loss: 0.079777\tBest loss: 0.073794\tAccuracy: 97.89%\n",
-      "13\tValidation loss: 0.080277\tBest loss: 0.073794\tAccuracy: 97.54%\n",
-      "14\tValidation loss: 0.072409\tBest loss: 0.072409\tAccuracy: 98.08%\n",
-      "15\tValidation loss: 0.071988\tBest loss: 0.071988\tAccuracy: 98.12%\n",
-      "16\tValidation loss: 0.074609\tBest loss: 0.071988\tAccuracy: 97.93%\n",
-      "17\tValidation loss: 0.069488\tBest loss: 0.069488\tAccuracy: 98.28%\n",
-      "18\tValidation loss: 0.080863\tBest loss: 0.069488\tAccuracy: 98.40%\n",
-      "19\tValidation loss: 0.074966\tBest loss: 0.069488\tAccuracy: 98.20%\n",
-      "20\tValidation loss: 0.071082\tBest loss: 0.069488\tAccuracy: 98.12%\n",
-      "21\tValidation loss: 0.070138\tBest loss: 0.069488\tAccuracy: 98.20%\n",
-      "22\tValidation loss: 0.066032\tBest loss: 0.066032\tAccuracy: 98.28%\n",
-      "23\tValidation loss: 0.061130\tBest loss: 0.061130\tAccuracy: 98.36%\n",
-      "24\tValidation loss: 0.067107\tBest loss: 0.061130\tAccuracy: 98.16%\n",
-      "25\tValidation loss: 0.071372\tBest loss: 0.061130\tAccuracy: 98.16%\n",
-      "26\tValidation loss: 0.068535\tBest loss: 0.061130\tAccuracy: 98.36%\n",
-      "27\tValidation loss: 0.065336\tBest loss: 0.061130\tAccuracy: 98.48%\n",
-      "28\tValidation loss: 0.066783\tBest loss: 0.061130\tAccuracy: 98.40%\n",
-      "29\tValidation loss: 0.092769\tBest loss: 0.061130\tAccuracy: 97.77%\n",
-      "30\tValidation loss: 0.075746\tBest loss: 0.061130\tAccuracy: 98.01%\n",
-      "31\tValidation loss: 0.084024\tBest loss: 0.061130\tAccuracy: 97.81%\n",
-      "32\tValidation loss: 0.116428\tBest loss: 0.061130\tAccuracy: 98.44%\n",
-      "33\tValidation loss: 0.079498\tBest loss: 0.061130\tAccuracy: 97.89%\n",
-      "34\tValidation loss: 0.078189\tBest loss: 0.061130\tAccuracy: 97.97%\n",
-      "35\tValidation loss: 0.083723\tBest loss: 0.061130\tAccuracy: 97.81%\n",
-      "36\tValidation loss: 0.088210\tBest loss: 0.061130\tAccuracy: 97.19%\n",
-      "37\tValidation loss: 0.080040\tBest loss: 0.061130\tAccuracy: 97.93%\n",
-      "38\tValidation loss: 0.086932\tBest loss: 0.061130\tAccuracy: 97.89%\n",
-      "39\tValidation loss: 0.240580\tBest loss: 0.061130\tAccuracy: 91.67%\n",
-      "40\tValidation loss: 0.166662\tBest loss: 0.061130\tAccuracy: 94.29%\n",
-      "41\tValidation loss: 0.125562\tBest loss: 0.061130\tAccuracy: 97.15%\n",
-      "42\tValidation loss: 0.124890\tBest loss: 0.061130\tAccuracy: 95.82%\n",
-      "43\tValidation loss: 0.127020\tBest loss: 0.061130\tAccuracy: 96.76%\n",
-      "44\tValidation loss: 0.121540\tBest loss: 0.061130\tAccuracy: 96.05%\n",
-      "Early stopping!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7fd9b2368d08>,\n",
-       "       batch_norm_momentum=None, batch_size=500, dropout_rate=0.5,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7fd9d5e628c8>,\n",
-       "       learning_rate=0.01, n_hidden_layers=5, n_neurons=90,\n",
-       "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
-       "       random_state=42)"
-      ]
-     },
-     "execution_count": 134,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dnn_clf_dropout = DNNClassifier(activation=leaky_relu(alpha=0.1), batch_size=500, learning_rate=0.01,\n",
     "                                n_neurons=90, random_state=42,\n",
@@ -5434,18 +3584,7 @@
    "cell_type": "code",
    "execution_count": 135,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.98657326328079398"
-      ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_pred = dnn_clf_dropout.predict(X_test1)\n",
     "accuracy_score(y_test1, y_pred)"
@@ -5462,255 +3601,7 @@
    "cell_type": "code",
    "execution_count": 136,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
-      "[CV] dropout_rate=0.5, n_neurons=70, learning_rate=0.01, activation=<function relu at 0x7fd9e8a660d0>, batch_size=100 \n",
-      "0\tValidation loss: 0.355079\tBest loss: 0.355079\tAccuracy: 91.44%\n",
-      "1\tValidation loss: 0.280624\tBest loss: 0.280624\tAccuracy: 94.10%\n",
-      "2\tValidation loss: 0.279819\tBest loss: 0.279819\tAccuracy: 92.77%\n",
-      "3\tValidation loss: 0.223614\tBest loss: 0.223614\tAccuracy: 94.10%\n",
-      "4\tValidation loss: 0.199802\tBest loss: 0.199802\tAccuracy: 95.11%\n",
-      "5\tValidation loss: 0.214481\tBest loss: 0.199802\tAccuracy: 95.47%\n",
-      "6\tValidation loss: 0.216195\tBest loss: 0.199802\tAccuracy: 95.78%\n",
-      "7\tValidation loss: 0.209172\tBest loss: 0.199802\tAccuracy: 94.80%\n",
-      "8\tValidation loss: 0.182841\tBest loss: 0.182841\tAccuracy: 95.70%\n",
-      "9\tValidation loss: 0.214252\tBest loss: 0.182841\tAccuracy: 95.82%\n",
-      "10\tValidation loss: 0.198762\tBest loss: 0.182841\tAccuracy: 95.62%\n",
-      "11\tValidation loss: 0.186415\tBest loss: 0.182841\tAccuracy: 95.82%\n",
-      "12\tValidation loss: 0.222924\tBest loss: 0.182841\tAccuracy: 96.05%\n",
-      "13\tValidation loss: 0.199636\tBest loss: 0.182841\tAccuracy: 95.97%\n",
-      "14\tValidation loss: 0.214436\tBest loss: 0.182841\tAccuracy: 95.97%\n",
-      "15\tValidation loss: 0.213507\tBest loss: 0.182841\tAccuracy: 95.47%\n",
-      "16\tValidation loss: 0.191497\tBest loss: 0.182841\tAccuracy: 95.78%\n",
-      "17\tValidation loss: 0.179503\tBest loss: 0.179503\tAccuracy: 95.93%\n",
-      "18\tValidation loss: 0.210343\tBest loss: 0.179503\tAccuracy: 95.74%\n",
-      "19\tValidation loss: 0.212626\tBest loss: 0.179503\tAccuracy: 95.27%\n",
-      "20\tValidation loss: 0.187110\tBest loss: 0.179503\tAccuracy: 96.09%\n",
-      "21\tValidation loss: 0.175171\tBest loss: 0.175171\tAccuracy: 95.78%\n",
-      "22\tValidation loss: 0.217172\tBest loss: 0.175171\tAccuracy: 95.66%\n",
-      "23\tValidation loss: 0.181060\tBest loss: 0.175171\tAccuracy: 96.44%\n",
-      "24\tValidation loss: 0.163630\tBest loss: 0.163630\tAccuracy: 95.93%\n",
-      "25\tValidation loss: 0.225873\tBest loss: 0.163630\tAccuracy: 95.58%\n",
-      "26\tValidation loss: 0.204975\tBest loss: 0.163630\tAccuracy: 95.66%\n",
-      "27\tValidation loss: 0.183588\tBest loss: 0.163630\tAccuracy: 95.97%\n",
-      "28\tValidation loss: 0.231080\tBest loss: 0.163630\tAccuracy: 95.11%\n",
-      "29\tValidation loss: 0.204342\tBest loss: 0.163630\tAccuracy: 95.74%\n",
-      "30\tValidation loss: 0.183963\tBest loss: 0.163630\tAccuracy: 95.93%\n",
-      "31\tValidation loss: 0.200975\tBest loss: 0.163630\tAccuracy: 95.23%\n",
-      "32\tValidation loss: 0.211165\tBest loss: 0.163630\tAccuracy: 95.23%\n",
-      "33\tValidation loss: 0.217777\tBest loss: 0.163630\tAccuracy: 95.07%\n",
-      "34\tValidation loss: 0.193184\tBest loss: 0.163630\tAccuracy: 95.39%\n",
-      "35\tValidation loss: 0.203809\tBest loss: 0.163630\tAccuracy: 95.58%\n",
-      "36\tValidation loss: 0.221673\tBest loss: 0.163630\tAccuracy: 94.57%\n",
-      "37\tValidation loss: 0.215750\tBest loss: 0.163630\tAccuracy: 95.39%\n",
-      "38\tValidation loss: 0.189653\tBest loss: 0.163630\tAccuracy: 96.09%\n",
-      "39\tValidation loss: 0.191333\tBest loss: 0.163630\tAccuracy: 95.19%\n",
-      "40\tValidation loss: 0.207714\tBest loss: 0.163630\tAccuracy: 96.01%\n",
-      "41\tValidation loss: 0.174490\tBest loss: 0.163630\tAccuracy: 95.39%\n",
-      "42\tValidation loss: 0.177445\tBest loss: 0.163630\tAccuracy: 95.82%\n",
-      "43\tValidation loss: 0.166708\tBest loss: 0.163630\tAccuracy: 96.09%\n",
-      "44\tValidation loss: 0.190829\tBest loss: 0.163630\tAccuracy: 95.70%\n",
-      "45\tValidation loss: 0.225985\tBest loss: 0.163630\tAccuracy: 96.25%\n",
-      "Early stopping!\n",
-      "[CV]  dropout_rate=0.5, n_neurons=70, learning_rate=0.01, activation=<function relu at 0x7fd9e8a660d0>, batch_size=100, total=  39.0s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   39.1s remaining:    0.0s\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[CV] dropout_rate=0.5, n_neurons=70, learning_rate=0.01, activation=<function relu at 0x7fd9e8a660d0>, batch_size=100 \n",
-      "0\tValidation loss: 0.748480\tBest loss: 0.748480\tAccuracy: 57.70%\n",
-      "1\tValidation loss: 0.516088\tBest loss: 0.516088\tAccuracy: 78.50%\n",
-      "2\tValidation loss: 0.448866\tBest loss: 0.448866\tAccuracy: 78.89%\n",
-      "3\tValidation loss: 0.435606\tBest loss: 0.435606\tAccuracy: 78.54%\n",
-      "4\tValidation loss: 0.435243\tBest loss: 0.435243\tAccuracy: 79.40%\n",
-      "5\tValidation loss: 0.450605\tBest loss: 0.435243\tAccuracy: 78.42%\n",
-      "6\tValidation loss: 0.430706\tBest loss: 0.430706\tAccuracy: 78.62%\n",
-      "7\tValidation loss: 0.449289\tBest loss: 0.430706\tAccuracy: 78.30%\n",
-      "8\tValidation loss: 0.413226\tBest loss: 0.413226\tAccuracy: 79.05%\n",
-      "9\tValidation loss: 0.436053\tBest loss: 0.413226\tAccuracy: 78.46%\n",
-      "10\tValidation loss: 0.459932\tBest loss: 0.413226\tAccuracy: 79.24%\n",
-      "11\tValidation loss: 0.424138\tBest loss: 0.413226\tAccuracy: 79.24%\n",
-      "12\tValidation loss: 0.409538\tBest loss: 0.409538\tAccuracy: 79.55%\n",
-      "13\tValidation loss: 0.416324\tBest loss: 0.409538\tAccuracy: 75.41%\n",
-      "14\tValidation loss: 0.440273\tBest loss: 0.409538\tAccuracy: 78.46%\n",
-      "15\tValidation loss: 0.435736\tBest loss: 0.409538\tAccuracy: 79.05%\n",
-      "16\tValidation loss: 0.428412\tBest loss: 0.409538\tAccuracy: 79.20%\n",
-      "17\tValidation loss: 0.450156\tBest loss: 0.409538\tAccuracy: 80.02%\n",
-      "18\tValidation loss: 0.421057\tBest loss: 0.409538\tAccuracy: 79.24%\n",
-      "19\tValidation loss: 0.442284\tBest loss: 0.409538\tAccuracy: 79.01%\n",
-      "20\tValidation loss: 0.426907\tBest loss: 0.409538\tAccuracy: 79.16%\n",
-      "21\tValidation loss: 0.439567\tBest loss: 0.409538\tAccuracy: 79.05%\n",
-      "22\tValidation loss: 0.452601\tBest loss: 0.409538\tAccuracy: 79.67%\n",
-      "23\tValidation loss: 0.424887\tBest loss: 0.409538\tAccuracy: 79.09%\n",
-      "24\tValidation loss: 0.441096\tBest loss: 0.409538\tAccuracy: 78.97%\n",
-      "25\tValidation loss: 0.417390\tBest loss: 0.409538\tAccuracy: 78.89%\n",
-      "26\tValidation loss: 0.418550\tBest loss: 0.409538\tAccuracy: 79.05%\n",
-      "27\tValidation loss: 0.426065\tBest loss: 0.409538\tAccuracy: 78.66%\n",
-      "28\tValidation loss: 0.413968\tBest loss: 0.409538\tAccuracy: 79.36%\n",
-      "29\tValidation loss: 0.425434\tBest loss: 0.409538\tAccuracy: 79.24%\n",
-      "30\tValidation loss: 0.455391\tBest loss: 0.409538\tAccuracy: 74.71%\n",
-      "31\tValidation loss: 0.429498\tBest loss: 0.409538\tAccuracy: 79.20%\n",
-      "32\tValidation loss: 0.427383\tBest loss: 0.409538\tAccuracy: 79.52%\n",
-      "33\tValidation loss: 0.422621\tBest loss: 0.409538\tAccuracy: 78.62%\n",
-      "Early stopping!\n",
-      "[CV]  dropout_rate=0.5, n_neurons=70, learning_rate=0.01, activation=<function relu at 0x7fd9e8a660d0>, batch_size=100, total=  27.4s\n",
-      "[CV] dropout_rate=0.5, n_neurons=70, learning_rate=0.01, activation=<function relu at 0x7fd9e8a660d0>, batch_size=100 \n",
-      "0\tValidation loss: 0.497714\tBest loss: 0.497714\tAccuracy: 86.71%\n",
-      "1\tValidation loss: 0.248258\tBest loss: 0.248258\tAccuracy: 93.51%\n",
-      "2\tValidation loss: 0.279785\tBest loss: 0.248258\tAccuracy: 93.71%\n",
-      "3\tValidation loss: 0.248663\tBest loss: 0.248258\tAccuracy: 94.61%\n",
-      "4\tValidation loss: 0.269139\tBest loss: 0.248258\tAccuracy: 94.76%\n",
-      "5\tValidation loss: 0.188808\tBest loss: 0.188808\tAccuracy: 95.39%\n",
-      "6\tValidation loss: 0.196049\tBest loss: 0.188808\tAccuracy: 95.58%\n",
-      "7\tValidation loss: 0.204966\tBest loss: 0.188808\tAccuracy: 95.15%\n",
-      "8\tValidation loss: 0.238414\tBest loss: 0.188808\tAccuracy: 94.61%\n",
-      "9\tValidation loss: 0.192095\tBest loss: 0.188808\tAccuracy: 95.97%\n",
-      "[...and much later...]\n",
-      "19\tValidation loss: 1.939112\tBest loss: 1.619874\tAccuracy: 22.01%\n",
-      "20\tValidation loss: 1.825761\tBest loss: 1.619874\tAccuracy: 19.27%\n",
-      "21\tValidation loss: 1.732937\tBest loss: 1.619874\tAccuracy: 22.01%\n",
-      "22\tValidation loss: 1.832995\tBest loss: 1.619874\tAccuracy: 20.91%\n",
-      "23\tValidation loss: 1.659557\tBest loss: 1.619874\tAccuracy: 20.91%\n",
-      "24\tValidation loss: 1.828380\tBest loss: 1.619874\tAccuracy: 18.73%\n",
-      "25\tValidation loss: 1.719589\tBest loss: 1.619874\tAccuracy: 22.01%\n",
-      "26\tValidation loss: 1.842429\tBest loss: 1.619874\tAccuracy: 18.73%\n",
-      "27\tValidation loss: 1.717596\tBest loss: 1.619874\tAccuracy: 19.27%\n",
-      "28\tValidation loss: 1.863441\tBest loss: 1.619874\tAccuracy: 19.08%\n",
-      "29\tValidation loss: 1.952335\tBest loss: 1.619874\tAccuracy: 19.08%\n",
-      "30\tValidation loss: 1.853776\tBest loss: 1.619874\tAccuracy: 20.91%\n",
-      "31\tValidation loss: 1.894134\tBest loss: 1.619874\tAccuracy: 22.01%\n",
-      "32\tValidation loss: 1.711688\tBest loss: 1.619874\tAccuracy: 19.08%\n",
-      "33\tValidation loss: 1.651240\tBest loss: 1.619874\tAccuracy: 18.73%\n",
-      "34\tValidation loss: 1.760639\tBest loss: 1.619874\tAccuracy: 20.91%\n",
-      "35\tValidation loss: 1.667938\tBest loss: 1.619874\tAccuracy: 22.01%\n",
-      "36\tValidation loss: 1.641116\tBest loss: 1.619874\tAccuracy: 20.91%\n",
-      "37\tValidation loss: 1.694960\tBest loss: 1.619874\tAccuracy: 19.08%\n",
-      "38\tValidation loss: 1.816517\tBest loss: 1.619874\tAccuracy: 18.73%\n",
-      "39\tValidation loss: 1.647246\tBest loss: 1.619874\tAccuracy: 18.73%\n",
-      "Early stopping!\n",
-      "[CV]  dropout_rate=0.5, n_neurons=140, learning_rate=0.05, activation=<function elu at 0x7fd9e8a620d0>, batch_size=100, total= 1.0min\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 130.6min finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\tValidation loss: 0.165751\tBest loss: 0.165751\tAccuracy: 95.47%\n",
-      "1\tValidation loss: 0.111834\tBest loss: 0.111834\tAccuracy: 96.99%\n",
-      "2\tValidation loss: 0.102867\tBest loss: 0.102867\tAccuracy: 96.83%\n",
-      "3\tValidation loss: 0.089197\tBest loss: 0.089197\tAccuracy: 97.85%\n",
-      "4\tValidation loss: 0.093953\tBest loss: 0.089197\tAccuracy: 97.77%\n",
-      "5\tValidation loss: 0.079498\tBest loss: 0.079498\tAccuracy: 98.08%\n",
-      "6\tValidation loss: 0.081214\tBest loss: 0.079498\tAccuracy: 98.01%\n",
-      "7\tValidation loss: 0.086096\tBest loss: 0.079498\tAccuracy: 97.77%\n",
-      "8\tValidation loss: 0.074422\tBest loss: 0.074422\tAccuracy: 97.73%\n",
-      "9\tValidation loss: 0.079650\tBest loss: 0.074422\tAccuracy: 97.89%\n",
-      "10\tValidation loss: 0.077278\tBest loss: 0.074422\tAccuracy: 97.77%\n",
-      "11\tValidation loss: 0.077608\tBest loss: 0.074422\tAccuracy: 98.24%\n",
-      "12\tValidation loss: 0.074337\tBest loss: 0.074337\tAccuracy: 98.05%\n",
-      "13\tValidation loss: 0.066028\tBest loss: 0.066028\tAccuracy: 98.28%\n",
-      "14\tValidation loss: 0.072845\tBest loss: 0.066028\tAccuracy: 98.16%\n",
-      "15\tValidation loss: 0.066652\tBest loss: 0.066028\tAccuracy: 98.05%\n",
-      "16\tValidation loss: 0.065729\tBest loss: 0.065729\tAccuracy: 98.16%\n",
-      "17\tValidation loss: 0.061191\tBest loss: 0.061191\tAccuracy: 98.51%\n",
-      "18\tValidation loss: 0.062528\tBest loss: 0.061191\tAccuracy: 98.44%\n",
-      "19\tValidation loss: 0.065407\tBest loss: 0.061191\tAccuracy: 98.36%\n",
-      "20\tValidation loss: 0.065273\tBest loss: 0.061191\tAccuracy: 98.44%\n",
-      "21\tValidation loss: 0.061035\tBest loss: 0.061035\tAccuracy: 98.40%\n",
-      "22\tValidation loss: 0.056312\tBest loss: 0.056312\tAccuracy: 98.59%\n",
-      "23\tValidation loss: 0.069074\tBest loss: 0.056312\tAccuracy: 98.40%\n",
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-      "25\tValidation loss: 0.068342\tBest loss: 0.056312\tAccuracy: 98.44%\n",
-      "26\tValidation loss: 0.063494\tBest loss: 0.056312\tAccuracy: 98.48%\n",
-      "27\tValidation loss: 0.057257\tBest loss: 0.056312\tAccuracy: 98.51%\n",
-      "28\tValidation loss: 0.058659\tBest loss: 0.056312\tAccuracy: 98.59%\n",
-      "29\tValidation loss: 0.059009\tBest loss: 0.056312\tAccuracy: 98.48%\n",
-      "30\tValidation loss: 0.058227\tBest loss: 0.056312\tAccuracy: 98.55%\n",
-      "31\tValidation loss: 0.062198\tBest loss: 0.056312\tAccuracy: 98.44%\n",
-      "32\tValidation loss: 0.058043\tBest loss: 0.056312\tAccuracy: 98.40%\n",
-      "33\tValidation loss: 0.055970\tBest loss: 0.055970\tAccuracy: 98.51%\n",
-      "34\tValidation loss: 0.060111\tBest loss: 0.055970\tAccuracy: 98.67%\n",
-      "35\tValidation loss: 0.058786\tBest loss: 0.055970\tAccuracy: 98.44%\n",
-      "36\tValidation loss: 0.059944\tBest loss: 0.055970\tAccuracy: 98.32%\n",
-      "37\tValidation loss: 0.058087\tBest loss: 0.055970\tAccuracy: 98.63%\n",
-      "38\tValidation loss: 0.063003\tBest loss: 0.055970\tAccuracy: 98.36%\n",
-      "39\tValidation loss: 0.052073\tBest loss: 0.052073\tAccuracy: 98.67%\n",
-      "40\tValidation loss: 0.058115\tBest loss: 0.052073\tAccuracy: 98.40%\n",
-      "41\tValidation loss: 0.059997\tBest loss: 0.052073\tAccuracy: 98.63%\n",
-      "42\tValidation loss: 0.052416\tBest loss: 0.052073\tAccuracy: 98.75%\n",
-      "43\tValidation loss: 0.053840\tBest loss: 0.052073\tAccuracy: 98.59%\n",
-      "44\tValidation loss: 0.054563\tBest loss: 0.052073\tAccuracy: 98.67%\n",
-      "45\tValidation loss: 0.049410\tBest loss: 0.049410\tAccuracy: 98.55%\n",
-      "46\tValidation loss: 0.057060\tBest loss: 0.049410\tAccuracy: 98.24%\n",
-      "47\tValidation loss: 0.062434\tBest loss: 0.049410\tAccuracy: 98.48%\n",
-      "48\tValidation loss: 0.054523\tBest loss: 0.049410\tAccuracy: 98.59%\n",
-      "49\tValidation loss: 0.052774\tBest loss: 0.049410\tAccuracy: 98.36%\n",
-      "50\tValidation loss: 0.056562\tBest loss: 0.049410\tAccuracy: 98.32%\n",
-      "51\tValidation loss: 0.060280\tBest loss: 0.049410\tAccuracy: 98.51%\n",
-      "52\tValidation loss: 0.055685\tBest loss: 0.049410\tAccuracy: 98.55%\n",
-      "53\tValidation loss: 0.056077\tBest loss: 0.049410\tAccuracy: 98.44%\n",
-      "54\tValidation loss: 0.057951\tBest loss: 0.049410\tAccuracy: 98.44%\n",
-      "55\tValidation loss: 0.056315\tBest loss: 0.049410\tAccuracy: 98.75%\n",
-      "56\tValidation loss: 0.055744\tBest loss: 0.049410\tAccuracy: 98.55%\n",
-      "57\tValidation loss: 0.054228\tBest loss: 0.049410\tAccuracy: 98.48%\n",
-      "58\tValidation loss: 0.057836\tBest loss: 0.049410\tAccuracy: 98.71%\n",
-      "59\tValidation loss: 0.053361\tBest loss: 0.049410\tAccuracy: 98.71%\n",
-      "60\tValidation loss: 0.056389\tBest loss: 0.049410\tAccuracy: 98.48%\n",
-      "61\tValidation loss: 0.061350\tBest loss: 0.049410\tAccuracy: 98.48%\n",
-      "62\tValidation loss: 0.052135\tBest loss: 0.049410\tAccuracy: 98.67%\n",
-      "63\tValidation loss: 0.053853\tBest loss: 0.049410\tAccuracy: 98.48%\n",
-      "64\tValidation loss: 0.056641\tBest loss: 0.049410\tAccuracy: 98.71%\n",
-      "65\tValidation loss: 0.052790\tBest loss: 0.049410\tAccuracy: 98.63%\n",
-      "66\tValidation loss: 0.053514\tBest loss: 0.049410\tAccuracy: 98.44%\n",
-      "Early stopping!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "RandomizedSearchCV(cv=None, error_score='raise',\n",
-       "          estimator=DNNClassifier(activation=<function elu at 0x7fd9e8a620d0>,\n",
-       "       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7fd9d5e628c8>,\n",
-       "       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\n",
-       "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
-       "       random_state=42),\n",
-       "          fit_params={'y_valid': array([0, 4, ..., 1, 2], dtype=uint8), 'X_valid': array([[ 0.,  0., ...,  0.,  0.],\n",
-       "       [ 0.,  0., ...,  0.,  0.],\n",
-       "       ...,\n",
-       "       [ 0.,  0., ...,  0.,  0.],\n",
-       "       [ 0.,  0., ...,  0.,  0.]], dtype=float32), 'n_epochs': 1000},\n",
-       "          iid=True, n_iter=50, n_jobs=1,\n",
-       "          param_distributions={'dropout_rate': [0.2, 0.3, 0.4, 0.5, 0.6], 'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'activation': [<function relu at 0x7fd9e8a660d0>, <function elu at 0x7fd9e8a620d0>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7fd9b2368950>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7fd9b23687b8>], 'batch_size': [10, 50, 100, 500]},\n",
-       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
-       "          return_train_score=True, scoring=None, verbose=2)"
-      ]
-     },
-     "execution_count": 136,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import RandomizedSearchCV\n",
     "\n",
@@ -5735,22 +3626,7 @@
    "cell_type": "code",
    "execution_count": 137,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'activation': <function __main__.leaky_relu.<locals>.parametrized_leaky_relu>,\n",
-       " 'batch_size': 500,\n",
-       " 'dropout_rate': 0.4,\n",
-       " 'learning_rate': 0.01,\n",
-       " 'n_neurons': 50}"
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "rnd_search_dropout.best_params_"
    ]
@@ -5759,18 +3635,7 @@
    "cell_type": "code",
    "execution_count": 138,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.98812998637867289"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_pred = rnd_search_dropout.predict(X_test1)\n",
     "accuracy_score(y_test1, y_pred)"
@@ -5963,68 +3828,7 @@
    "cell_type": "code",
    "execution_count": 145,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_best_mnist_model_0_to_4\n",
-      "0\tValidation loss: 0.967851\tBest loss: 0.967851\tAccuracy: 67.33%\n",
-      "1\tValidation loss: 0.861747\tBest loss: 0.861747\tAccuracy: 71.33%\n",
-      "2\tValidation loss: 0.777535\tBest loss: 0.777535\tAccuracy: 72.00%\n",
-      "3\tValidation loss: 0.699915\tBest loss: 0.699915\tAccuracy: 75.33%\n",
-      "4\tValidation loss: 0.786714\tBest loss: 0.699915\tAccuracy: 78.00%\n",
-      "5\tValidation loss: 0.735406\tBest loss: 0.699915\tAccuracy: 76.67%\n",
-      "6\tValidation loss: 0.732264\tBest loss: 0.699915\tAccuracy: 78.00%\n",
-      "7\tValidation loss: 0.691741\tBest loss: 0.691741\tAccuracy: 76.00%\n",
-      "8\tValidation loss: 0.672757\tBest loss: 0.672757\tAccuracy: 80.00%\n",
-      "9\tValidation loss: 0.666520\tBest loss: 0.666520\tAccuracy: 80.00%\n",
-      "10\tValidation loss: 0.639375\tBest loss: 0.639375\tAccuracy: 81.33%\n",
-      "11\tValidation loss: 0.645089\tBest loss: 0.639375\tAccuracy: 82.00%\n",
-      "12\tValidation loss: 0.646768\tBest loss: 0.639375\tAccuracy: 80.00%\n",
-      "13\tValidation loss: 0.623784\tBest loss: 0.623784\tAccuracy: 82.67%\n",
-      "14\tValidation loss: 0.663026\tBest loss: 0.623784\tAccuracy: 80.00%\n",
-      "15\tValidation loss: 0.704513\tBest loss: 0.623784\tAccuracy: 79.33%\n",
-      "16\tValidation loss: 0.684003\tBest loss: 0.623784\tAccuracy: 79.33%\n",
-      "17\tValidation loss: 0.658575\tBest loss: 0.623784\tAccuracy: 82.67%\n",
-      "18\tValidation loss: 0.669875\tBest loss: 0.623784\tAccuracy: 79.33%\n",
-      "19\tValidation loss: 0.664581\tBest loss: 0.623784\tAccuracy: 78.67%\n",
-      "20\tValidation loss: 0.653490\tBest loss: 0.623784\tAccuracy: 80.00%\n",
-      "21\tValidation loss: 0.707304\tBest loss: 0.623784\tAccuracy: 79.33%\n",
-      "22\tValidation loss: 0.706012\tBest loss: 0.623784\tAccuracy: 80.67%\n",
-      "23\tValidation loss: 0.681227\tBest loss: 0.623784\tAccuracy: 78.67%\n",
-      "24\tValidation loss: 0.786823\tBest loss: 0.623784\tAccuracy: 78.00%\n",
-      "25\tValidation loss: 0.686110\tBest loss: 0.623784\tAccuracy: 79.33%\n",
-      "26\tValidation loss: 0.675166\tBest loss: 0.623784\tAccuracy: 82.67%\n",
-      "27\tValidation loss: 0.667711\tBest loss: 0.623784\tAccuracy: 82.67%\n",
-      "28\tValidation loss: 0.612220\tBest loss: 0.612220\tAccuracy: 83.33%\n",
-      "29\tValidation loss: 0.701196\tBest loss: 0.612220\tAccuracy: 78.00%\n",
-      "30\tValidation loss: 0.687806\tBest loss: 0.612220\tAccuracy: 81.33%\n",
-      "31\tValidation loss: 0.776596\tBest loss: 0.612220\tAccuracy: 79.33%\n",
-      "32\tValidation loss: 0.674172\tBest loss: 0.612220\tAccuracy: 80.00%\n",
-      "33\tValidation loss: 0.719044\tBest loss: 0.612220\tAccuracy: 83.33%\n",
-      "34\tValidation loss: 0.856403\tBest loss: 0.612220\tAccuracy: 74.00%\n",
-      "35\tValidation loss: 0.744627\tBest loss: 0.612220\tAccuracy: 80.00%\n",
-      "36\tValidation loss: 0.779348\tBest loss: 0.612220\tAccuracy: 78.00%\n",
-      "37\tValidation loss: 0.763777\tBest loss: 0.612220\tAccuracy: 78.00%\n",
-      "38\tValidation loss: 0.727376\tBest loss: 0.612220\tAccuracy: 78.00%\n",
-      "39\tValidation loss: 0.823514\tBest loss: 0.612220\tAccuracy: 78.00%\n",
-      "40\tValidation loss: 0.725053\tBest loss: 0.612220\tAccuracy: 80.67%\n",
-      "41\tValidation loss: 0.678497\tBest loss: 0.612220\tAccuracy: 80.67%\n",
-      "42\tValidation loss: 0.709977\tBest loss: 0.612220\tAccuracy: 80.67%\n",
-      "43\tValidation loss: 0.737200\tBest loss: 0.612220\tAccuracy: 77.33%\n",
-      "44\tValidation loss: 0.757937\tBest loss: 0.612220\tAccuracy: 77.33%\n",
-      "45\tValidation loss: 0.732024\tBest loss: 0.612220\tAccuracy: 80.00%\n",
-      "46\tValidation loss: 0.756428\tBest loss: 0.612220\tAccuracy: 80.67%\n",
-      "47\tValidation loss: 0.757610\tBest loss: 0.612220\tAccuracy: 78.67%\n",
-      "48\tValidation loss: 0.844137\tBest loss: 0.612220\tAccuracy: 80.00%\n",
-      "Early stopping!\n",
-      "Total training time: 2.3s\n",
-      "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_five_frozen\n",
-      "Final test accuracy: 76.30%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import time\n",
     "\n",
@@ -6120,54 +3924,7 @@
    "cell_type": "code",
    "execution_count": 147,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_best_mnist_model_0_to_4\n",
-      "0\tValidation loss: 1.109053\tBest loss: 1.109053\tAccuracy: 60.67%\n",
-      "1\tValidation loss: 0.813156\tBest loss: 0.813156\tAccuracy: 72.00%\n",
-      "2\tValidation loss: 0.755930\tBest loss: 0.755930\tAccuracy: 76.67%\n",
-      "3\tValidation loss: 0.744004\tBest loss: 0.744004\tAccuracy: 74.67%\n",
-      "4\tValidation loss: 0.685080\tBest loss: 0.685080\tAccuracy: 78.00%\n",
-      "5\tValidation loss: 0.702316\tBest loss: 0.685080\tAccuracy: 78.00%\n",
-      "6\tValidation loss: 0.646487\tBest loss: 0.646487\tAccuracy: 80.00%\n",
-      "7\tValidation loss: 0.686437\tBest loss: 0.646487\tAccuracy: 79.33%\n",
-      "8\tValidation loss: 0.750047\tBest loss: 0.646487\tAccuracy: 79.33%\n",
-      "9\tValidation loss: 0.688554\tBest loss: 0.646487\tAccuracy: 79.33%\n",
-      "10\tValidation loss: 0.785184\tBest loss: 0.646487\tAccuracy: 78.67%\n",
-      "11\tValidation loss: 0.634506\tBest loss: 0.634506\tAccuracy: 80.67%\n",
-      "12\tValidation loss: 0.656797\tBest loss: 0.634506\tAccuracy: 81.33%\n",
-      "13\tValidation loss: 0.645497\tBest loss: 0.634506\tAccuracy: 81.33%\n",
-      "14\tValidation loss: 0.618038\tBest loss: 0.618038\tAccuracy: 83.33%\n",
-      "15\tValidation loss: 0.641752\tBest loss: 0.618038\tAccuracy: 78.67%\n",
-      "16\tValidation loss: 0.645671\tBest loss: 0.618038\tAccuracy: 80.67%\n",
-      "17\tValidation loss: 0.654640\tBest loss: 0.618038\tAccuracy: 82.00%\n",
-      "18\tValidation loss: 0.670569\tBest loss: 0.618038\tAccuracy: 79.33%\n",
-      "19\tValidation loss: 0.670985\tBest loss: 0.618038\tAccuracy: 82.00%\n",
-      "20\tValidation loss: 0.659538\tBest loss: 0.618038\tAccuracy: 82.67%\n",
-      "21\tValidation loss: 0.622648\tBest loss: 0.618038\tAccuracy: 83.33%\n",
-      "22\tValidation loss: 0.736155\tBest loss: 0.618038\tAccuracy: 79.33%\n",
-      "23\tValidation loss: 0.739367\tBest loss: 0.618038\tAccuracy: 76.67%\n",
-      "24\tValidation loss: 0.699710\tBest loss: 0.618038\tAccuracy: 78.00%\n",
-      "25\tValidation loss: 0.709630\tBest loss: 0.618038\tAccuracy: 81.33%\n",
-      "26\tValidation loss: 0.692474\tBest loss: 0.618038\tAccuracy: 79.33%\n",
-      "27\tValidation loss: 0.807931\tBest loss: 0.618038\tAccuracy: 77.33%\n",
-      "28\tValidation loss: 0.676134\tBest loss: 0.618038\tAccuracy: 82.00%\n",
-      "29\tValidation loss: 0.738905\tBest loss: 0.618038\tAccuracy: 79.33%\n",
-      "30\tValidation loss: 0.664826\tBest loss: 0.618038\tAccuracy: 81.33%\n",
-      "31\tValidation loss: 0.694714\tBest loss: 0.618038\tAccuracy: 80.00%\n",
-      "32\tValidation loss: 0.739238\tBest loss: 0.618038\tAccuracy: 83.33%\n",
-      "33\tValidation loss: 0.697210\tBest loss: 0.618038\tAccuracy: 80.00%\n",
-      "34\tValidation loss: 0.817373\tBest loss: 0.618038\tAccuracy: 79.33%\n",
-      "Early stopping!\n",
-      "Total training time: 0.9s\n",
-      "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_five_frozen\n",
-      "Final test accuracy: 76.51%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import time\n",
     "\n",
@@ -6299,71 +4056,7 @@
    "cell_type": "code",
    "execution_count": 150,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_best_mnist_model_0_to_4\n",
-      "0\tValidation loss: 0.923460\tBest loss: 0.923460\tAccuracy: 69.33%\n",
-      "1\tValidation loss: 0.796192\tBest loss: 0.796192\tAccuracy: 77.33%\n",
-      "2\tValidation loss: 0.812068\tBest loss: 0.796192\tAccuracy: 78.67%\n",
-      "3\tValidation loss: 0.697938\tBest loss: 0.697938\tAccuracy: 80.67%\n",
-      "4\tValidation loss: 0.877122\tBest loss: 0.697938\tAccuracy: 74.67%\n",
-      "5\tValidation loss: 0.708524\tBest loss: 0.697938\tAccuracy: 81.33%\n",
-      "6\tValidation loss: 0.689500\tBest loss: 0.689500\tAccuracy: 84.00%\n",
-      "7\tValidation loss: 0.758315\tBest loss: 0.689500\tAccuracy: 81.33%\n",
-      "8\tValidation loss: 0.711138\tBest loss: 0.689500\tAccuracy: 78.67%\n",
-      "9\tValidation loss: 0.687304\tBest loss: 0.687304\tAccuracy: 81.33%\n",
-      "10\tValidation loss: 0.639222\tBest loss: 0.639222\tAccuracy: 81.33%\n",
-      "11\tValidation loss: 0.716750\tBest loss: 0.639222\tAccuracy: 82.67%\n",
-      "12\tValidation loss: 0.693442\tBest loss: 0.639222\tAccuracy: 80.67%\n",
-      "13\tValidation loss: 0.727682\tBest loss: 0.639222\tAccuracy: 84.00%\n",
-      "14\tValidation loss: 0.637289\tBest loss: 0.637289\tAccuracy: 84.67%\n",
-      "15\tValidation loss: 0.741304\tBest loss: 0.637289\tAccuracy: 83.33%\n",
-      "16\tValidation loss: 0.651895\tBest loss: 0.637289\tAccuracy: 82.67%\n",
-      "17\tValidation loss: 0.641192\tBest loss: 0.637289\tAccuracy: 80.67%\n",
-      "18\tValidation loss: 0.690386\tBest loss: 0.637289\tAccuracy: 80.67%\n",
-      "19\tValidation loss: 0.648541\tBest loss: 0.637289\tAccuracy: 82.67%\n",
-      "20\tValidation loss: 0.779663\tBest loss: 0.637289\tAccuracy: 83.33%\n",
-      "21\tValidation loss: 0.768834\tBest loss: 0.637289\tAccuracy: 82.67%\n",
-      "22\tValidation loss: 0.706279\tBest loss: 0.637289\tAccuracy: 82.67%\n",
-      "23\tValidation loss: 0.745840\tBest loss: 0.637289\tAccuracy: 82.00%\n",
-      "24\tValidation loss: 0.740068\tBest loss: 0.637289\tAccuracy: 83.33%\n",
-      "25\tValidation loss: 0.604927\tBest loss: 0.604927\tAccuracy: 84.67%\n",
-      "26\tValidation loss: 0.635410\tBest loss: 0.604927\tAccuracy: 82.00%\n",
-      "27\tValidation loss: 0.776003\tBest loss: 0.604927\tAccuracy: 82.67%\n",
-      "28\tValidation loss: 0.621502\tBest loss: 0.604927\tAccuracy: 82.00%\n",
-      "29\tValidation loss: 0.695963\tBest loss: 0.604927\tAccuracy: 83.33%\n",
-      "30\tValidation loss: 0.668194\tBest loss: 0.604927\tAccuracy: 84.67%\n",
-      "31\tValidation loss: 0.768975\tBest loss: 0.604927\tAccuracy: 82.67%\n",
-      "32\tValidation loss: 0.594731\tBest loss: 0.594731\tAccuracy: 84.00%\n",
-      "33\tValidation loss: 0.665088\tBest loss: 0.594731\tAccuracy: 84.00%\n",
-      "34\tValidation loss: 0.716284\tBest loss: 0.594731\tAccuracy: 81.33%\n",
-      "35\tValidation loss: 0.782680\tBest loss: 0.594731\tAccuracy: 84.00%\n",
-      "36\tValidation loss: 0.816441\tBest loss: 0.594731\tAccuracy: 84.00%\n",
-      "37\tValidation loss: 0.749341\tBest loss: 0.594731\tAccuracy: 84.00%\n",
-      "38\tValidation loss: 0.728754\tBest loss: 0.594731\tAccuracy: 82.00%\n",
-      "39\tValidation loss: 0.838166\tBest loss: 0.594731\tAccuracy: 84.00%\n",
-      "40\tValidation loss: 0.714871\tBest loss: 0.594731\tAccuracy: 84.00%\n",
-      "41\tValidation loss: 0.765463\tBest loss: 0.594731\tAccuracy: 84.67%\n",
-      "42\tValidation loss: 0.744043\tBest loss: 0.594731\tAccuracy: 82.00%\n",
-      "43\tValidation loss: 0.726922\tBest loss: 0.594731\tAccuracy: 83.33%\n",
-      "44\tValidation loss: 0.641118\tBest loss: 0.594731\tAccuracy: 82.67%\n",
-      "45\tValidation loss: 0.657861\tBest loss: 0.594731\tAccuracy: 84.00%\n",
-      "46\tValidation loss: 0.803642\tBest loss: 0.594731\tAccuracy: 86.00%\n",
-      "47\tValidation loss: 0.754644\tBest loss: 0.594731\tAccuracy: 84.67%\n",
-      "48\tValidation loss: 0.865141\tBest loss: 0.594731\tAccuracy: 84.00%\n",
-      "49\tValidation loss: 0.709169\tBest loss: 0.594731\tAccuracy: 84.67%\n",
-      "50\tValidation loss: 0.723139\tBest loss: 0.594731\tAccuracy: 84.00%\n",
-      "51\tValidation loss: 0.745109\tBest loss: 0.594731\tAccuracy: 84.67%\n",
-      "52\tValidation loss: 0.803908\tBest loss: 0.594731\tAccuracy: 82.67%\n",
-      "Early stopping!\n",
-      "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_four_frozen\n",
-      "Final test accuracy: 80.17%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 1000\n",
     "batch_size = 20\n",
@@ -6443,43 +4136,7 @@
    "cell_type": "code",
    "execution_count": 152,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_four_frozen\n",
-      "0\tValidation loss: 0.880485\tBest loss: 0.880485\tAccuracy: 86.00%\n",
-      "1\tValidation loss: 1.388974\tBest loss: 0.880485\tAccuracy: 81.33%\n",
-      "2\tValidation loss: 0.741543\tBest loss: 0.741543\tAccuracy: 86.67%\n",
-      "3\tValidation loss: 1.030772\tBest loss: 0.741543\tAccuracy: 84.00%\n",
-      "4\tValidation loss: 0.699438\tBest loss: 0.699438\tAccuracy: 87.33%\n",
-      "5\tValidation loss: 0.743930\tBest loss: 0.699438\tAccuracy: 89.33%\n",
-      "6\tValidation loss: 1.711346\tBest loss: 0.699438\tAccuracy: 82.67%\n",
-      "7\tValidation loss: 1.437762\tBest loss: 0.699438\tAccuracy: 82.00%\n",
-      "8\tValidation loss: 0.829231\tBest loss: 0.699438\tAccuracy: 86.67%\n",
-      "9\tValidation loss: 1.033920\tBest loss: 0.699438\tAccuracy: 86.67%\n",
-      "10\tValidation loss: 1.055709\tBest loss: 0.699438\tAccuracy: 87.33%\n",
-      "11\tValidation loss: 0.971796\tBest loss: 0.699438\tAccuracy: 88.00%\n",
-      "12\tValidation loss: 0.801815\tBest loss: 0.699438\tAccuracy: 86.00%\n",
-      "13\tValidation loss: 0.726146\tBest loss: 0.699438\tAccuracy: 89.33%\n",
-      "14\tValidation loss: 0.757217\tBest loss: 0.699438\tAccuracy: 88.67%\n",
-      "15\tValidation loss: 0.791842\tBest loss: 0.699438\tAccuracy: 90.00%\n",
-      "16\tValidation loss: 0.732507\tBest loss: 0.699438\tAccuracy: 90.67%\n",
-      "17\tValidation loss: 0.737297\tBest loss: 0.699438\tAccuracy: 90.67%\n",
-      "18\tValidation loss: 0.746715\tBest loss: 0.699438\tAccuracy: 90.00%\n",
-      "19\tValidation loss: 0.747751\tBest loss: 0.699438\tAccuracy: 90.00%\n",
-      "20\tValidation loss: 0.749325\tBest loss: 0.699438\tAccuracy: 90.00%\n",
-      "21\tValidation loss: 0.751899\tBest loss: 0.699438\tAccuracy: 90.00%\n",
-      "22\tValidation loss: 0.754314\tBest loss: 0.699438\tAccuracy: 90.00%\n",
-      "23\tValidation loss: 0.757840\tBest loss: 0.699438\tAccuracy: 90.00%\n",
-      "24\tValidation loss: 0.761543\tBest loss: 0.699438\tAccuracy: 90.00%\n",
-      "Early stopping!\n",
-      "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_two_frozen\n",
-      "Final test accuracy: 84.37%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 1000\n",
     "batch_size = 20\n",
@@ -6544,52 +4201,7 @@
    "cell_type": "code",
    "execution_count": 154,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_two_frozen\n",
-      "0\tValidation loss: 0.846005\tBest loss: 0.846005\tAccuracy: 83.33%\n",
-      "1\tValidation loss: 0.694439\tBest loss: 0.694439\tAccuracy: 91.33%\n",
-      "2\tValidation loss: 1.201433\tBest loss: 0.694439\tAccuracy: 85.33%\n",
-      "3\tValidation loss: 1.975297\tBest loss: 0.694439\tAccuracy: 85.33%\n",
-      "4\tValidation loss: 0.692805\tBest loss: 0.692805\tAccuracy: 95.33%\n",
-      "5\tValidation loss: 1.090217\tBest loss: 0.692805\tAccuracy: 91.33%\n",
-      "6\tValidation loss: 1.924300\tBest loss: 0.692805\tAccuracy: 90.67%\n",
-      "7\tValidation loss: 4.019310\tBest loss: 0.692805\tAccuracy: 87.33%\n",
-      "8\tValidation loss: 4.150792\tBest loss: 0.692805\tAccuracy: 78.00%\n",
-      "9\tValidation loss: 4.522708\tBest loss: 0.692805\tAccuracy: 75.33%\n",
-      "10\tValidation loss: 1.163385\tBest loss: 0.692805\tAccuracy: 90.00%\n",
-      "11\tValidation loss: 0.655868\tBest loss: 0.655868\tAccuracy: 92.67%\n",
-      "12\tValidation loss: 0.943888\tBest loss: 0.655868\tAccuracy: 92.67%\n",
-      "13\tValidation loss: 0.529996\tBest loss: 0.529996\tAccuracy: 92.67%\n",
-      "14\tValidation loss: 0.610578\tBest loss: 0.529996\tAccuracy: 94.67%\n",
-      "15\tValidation loss: 3.899716\tBest loss: 0.529996\tAccuracy: 88.00%\n",
-      "16\tValidation loss: 18.285717\tBest loss: 0.529996\tAccuracy: 86.67%\n",
-      "17\tValidation loss: 23.169626\tBest loss: 0.529996\tAccuracy: 78.00%\n",
-      "18\tValidation loss: 17.309252\tBest loss: 0.529996\tAccuracy: 90.00%\n",
-      "19\tValidation loss: 44.261902\tBest loss: 0.529996\tAccuracy: 80.00%\n",
-      "20\tValidation loss: 52.460327\tBest loss: 0.529996\tAccuracy: 80.00%\n",
-      "21\tValidation loss: 26.318949\tBest loss: 0.529996\tAccuracy: 83.33%\n",
-      "22\tValidation loss: 32.857723\tBest loss: 0.529996\tAccuracy: 90.67%\n",
-      "23\tValidation loss: 53.359497\tBest loss: 0.529996\tAccuracy: 88.00%\n",
-      "24\tValidation loss: 57.823742\tBest loss: 0.529996\tAccuracy: 88.00%\n",
-      "25\tValidation loss: 37.154972\tBest loss: 0.529996\tAccuracy: 92.67%\n",
-      "26\tValidation loss: 41.386772\tBest loss: 0.529996\tAccuracy: 90.00%\n",
-      "27\tValidation loss: 43.486767\tBest loss: 0.529996\tAccuracy: 90.00%\n",
-      "28\tValidation loss: 42.776855\tBest loss: 0.529996\tAccuracy: 88.67%\n",
-      "29\tValidation loss: 43.368839\tBest loss: 0.529996\tAccuracy: 90.67%\n",
-      "30\tValidation loss: 43.440975\tBest loss: 0.529996\tAccuracy: 90.00%\n",
-      "31\tValidation loss: 42.889927\tBest loss: 0.529996\tAccuracy: 91.33%\n",
-      "32\tValidation loss: 42.806690\tBest loss: 0.529996\tAccuracy: 90.67%\n",
-      "33\tValidation loss: 42.784145\tBest loss: 0.529996\tAccuracy: 90.67%\n",
-      "Early stopping!\n",
-      "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_no_frozen\n",
-      "Final test accuracy: 90.60%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 1000\n",
     "batch_size = 20\n",
@@ -6637,61 +4249,7 @@
    "cell_type": "code",
    "execution_count": 155,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\tValidation loss: 0.803557\tBest loss: 0.803557\tAccuracy: 71.33%\n",
-      "1\tValidation loss: 0.966741\tBest loss: 0.803557\tAccuracy: 85.33%\n",
-      "2\tValidation loss: 1.158972\tBest loss: 0.803557\tAccuracy: 78.00%\n",
-      "3\tValidation loss: 0.615960\tBest loss: 0.615960\tAccuracy: 88.00%\n",
-      "4\tValidation loss: 0.612626\tBest loss: 0.612626\tAccuracy: 92.00%\n",
-      "5\tValidation loss: 0.686420\tBest loss: 0.612626\tAccuracy: 89.33%\n",
-      "6\tValidation loss: 0.805281\tBest loss: 0.612626\tAccuracy: 89.33%\n",
-      "7\tValidation loss: 0.753108\tBest loss: 0.612626\tAccuracy: 88.67%\n",
-      "8\tValidation loss: 1.051471\tBest loss: 0.612626\tAccuracy: 86.00%\n",
-      "9\tValidation loss: 0.487089\tBest loss: 0.487089\tAccuracy: 93.33%\n",
-      "10\tValidation loss: 1.191093\tBest loss: 0.487089\tAccuracy: 85.33%\n",
-      "11\tValidation loss: 0.878905\tBest loss: 0.487089\tAccuracy: 88.67%\n",
-      "12\tValidation loss: 0.768841\tBest loss: 0.487089\tAccuracy: 91.33%\n",
-      "13\tValidation loss: 1.153907\tBest loss: 0.487089\tAccuracy: 90.67%\n",
-      "14\tValidation loss: 0.985427\tBest loss: 0.487089\tAccuracy: 89.33%\n",
-      "15\tValidation loss: 1.221879\tBest loss: 0.487089\tAccuracy: 85.33%\n",
-      "16\tValidation loss: 0.961743\tBest loss: 0.487089\tAccuracy: 88.67%\n",
-      "17\tValidation loss: 3.116057\tBest loss: 0.487089\tAccuracy: 84.00%\n",
-      "18\tValidation loss: 0.686387\tBest loss: 0.487089\tAccuracy: 84.00%\n",
-      "19\tValidation loss: 0.929801\tBest loss: 0.487089\tAccuracy: 88.00%\n",
-      "20\tValidation loss: 1.137579\tBest loss: 0.487089\tAccuracy: 92.00%\n",
-      "21\tValidation loss: 0.987261\tBest loss: 0.487089\tAccuracy: 91.33%\n",
-      "22\tValidation loss: 2.030677\tBest loss: 0.487089\tAccuracy: 91.33%\n",
-      "23\tValidation loss: 1.094184\tBest loss: 0.487089\tAccuracy: 92.00%\n",
-      "24\tValidation loss: 1.332256\tBest loss: 0.487089\tAccuracy: 82.67%\n",
-      "25\tValidation loss: 1.128633\tBest loss: 0.487089\tAccuracy: 85.33%\n",
-      "26\tValidation loss: 0.866569\tBest loss: 0.487089\tAccuracy: 90.67%\n",
-      "27\tValidation loss: 1.088500\tBest loss: 0.487089\tAccuracy: 89.33%\n",
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-      "29\tValidation loss: 1.163180\tBest loss: 0.487089\tAccuracy: 89.33%\n",
-      "30\tValidation loss: 1.154797\tBest loss: 0.487089\tAccuracy: 89.33%\n",
-      "Early stopping!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "DNNClassifier(activation=<function elu at 0x7fd9e8a620d0>,\n",
-       "       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7fd9d5e628c8>,\n",
-       "       learning_rate=0.01, n_hidden_layers=4, n_neurons=100,\n",
-       "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
-       "       random_state=42)"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dnn_clf_5_to_9 = DNNClassifier(n_hidden_layers=4, random_state=42)\n",
     "dnn_clf_5_to_9.fit(X_train2, y_train2, n_epochs=1000, X_valid=X_valid2, y_valid=y_valid2)"
@@ -6701,18 +4259,7 @@
    "cell_type": "code",
    "execution_count": 156,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.90413495165603786"
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_pred = dnn_clf_5_to_9.predict(X_test2)\n",
     "accuracy_score(y_test2, y_pred)"
@@ -6843,18 +4390,7 @@
    "cell_type": "code",
    "execution_count": 161,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TensorShape([Dimension(None), Dimension(100)])"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dnn1.shape"
    ]
@@ -6863,18 +4399,7 @@
    "cell_type": "code",
    "execution_count": 162,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TensorShape([Dimension(None), Dimension(100)])"
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dnn2.shape"
    ]
@@ -6890,18 +4415,7 @@
    "cell_type": "code",
    "execution_count": 163,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TensorShape([Dimension(None), Dimension(200)])"
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dnn_outputs.shape"
    ]
@@ -7123,18 +4637,7 @@
    "cell_type": "code",
    "execution_count": 173,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((5, 2, 784), dtype('float32'))"
-      ]
-     },
-     "execution_count": 173,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "X_batch.shape, X_batch.dtype"
    ]
@@ -7150,18 +4653,7 @@
    "cell_type": "code",
    "execution_count": 174,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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m5vp9rf6ZLVq0SHLx4nExH/BsIZGXWFxERni2MAzvvvuuZN0K6uVNrp2JUStYJJw/fx4A\n8Nhjj8mxQK2gptvGOGkFw1Y0R01UBHDmCoFbRDpw4MCAr+sNavr37x+TMRUl69atAwC88847V/za\nIUOGAADGjRtnOaSY4MxFZITFRWQk4dvCqVOnStYnHnr06AEgtKVJri3U2ypr7r0A3wfhJTN9wmLM\nmDGX/Vp9P1avXr0AABUqVLAZWAxx5iIywuIiMpKQy590Kzhs2DDJui10q97XrFkjx2rUqCH5N7/5\njeTnn3/e7zNq1aolWb/HlVbTG/J8+VNOTo7ke++9V/KuXbsu+336IXZF5Poglz8ReYnFRWQkIdtC\nvf/FnXfeKVk/LcPRbZxecqP3Hw8kOzs74Ht4yPO2sGvXrpKvdMH4ySeflDxt2jTJpUqVitZwLLEt\nJPJSQs5c2t69eyXrfywH2nZa/ywC7TX43HPPSY7D5Tmez1x6C+pA17YyMjIk65m/COLMReQlFheR\nkYRf/qQf6akf+fnss88CAGbOnBnw+2rWrCnZtTi/+MUvLIaYMIKd2KlTpw4A3+tZyYAzF5ERFheR\nkYQ/W5hEPD9bmER4tpDISywuIiMsLiIjLC4iIywuIiMsLiIjLC4iIywuIiMsLiIjLC4iIywuIiMs\nLiIjLC4iIywuIiMsLiIjLC4iIywuIiMsLiIjLC4iIywuIiMJv28hea9ly5YAgKNHj8ox/dAGvbX1\npk2bAAAnT56M0ejscOYiMsLiIjLCfQsTR9zuW3jrrbcCALZu3XrFry1e/Ie/78uWLSvH2rVrJ/me\ne+7x+56f//znkitVqhTxOMPAfQuJvMTiIjLCs4VkrlmzZgBCawsvXboEADhz5owce/fddyUvW7bM\n73vS0tIk9+zZM+JxRhtnLiIjLC4iI0nfFur2Qz8/WT+obdasWX7fd9ttt0nWD9grXbo0AKBfv35y\nrGLFitEZbBE1ZcoUAMDu3bvlmLtYHA2fffZZ1N4rmjhzERlJ+utcAwYMkPzaa6+F/H3651asmP9l\nj2rVqknW12l++ctfSq5SpQoA35mvEOL2Opc7kaGvUZ0+fTrg12ZkZAAAateuLceC/axHjhwJAGjd\nurUcc52DMV7nIvISi4vISNK3hZ9++qnkYG3hqlWrAABffvmlHLtSWxiM/r4KFSoAAGrVqiXHdOvU\nuXNnybq1DCJu28KHH34YAPDmm28GfL1p06aSFy9eDMD3ZxKH2BYSeYnFRWQk6dtC7dSpU5JHjx4t\necaMGX5f+8orr0jWLcz+/fsBAFlZWXLs2LFjko8cOSI5nHbSLQu6jLhtC6+0Kl7/rB555BHr4UQD\n20IiL7G4iIwk/fKnNWvWSB46dKhkfRbRtW/Dhw+XY126dJF8zTXX+L3vsGHDJH/99deS9RnHRLZ2\n7VrJBw4c8Htd/8zatGkTkzHFGmcuIiNJdULjwoULkjds2AAAeOCBB+RYfn6+ZLc0CQCeeuopAL5L\nl6pWrWoxxMKIqxMaDRs2lLxv3z6/1ytXriz57bfflnzLLbdc9n2vvvpqyeGcEIoyntAg8hKLi8hI\nUrWF69atk3zXXXf98EFBljFNmzZN8sCBAy2GE21Fqi0Mh/4d6RNFY8eOBQCUL1++UO8fAbaFRF5i\ncREZSaq28IsvvpDsbrDTS5N0W1iiRMElwPr16/u91549eyyGWBhx1RbqM6t/+tOf/F5v3769ZN2u\nB3KlOxAaNWok2d3BAADp6emhDDUSbAuJvJRUM5deKeHuG/rggw/k2CeffCL5m2++kZybm+v3XnrT\nGfcPa6Bga2V9m3+MxNXMdfHiRcnuZ63vVUtJSZGsry8GohdRv/7665L1dUtHb4O9dOlSybfffnso\nww4VZy4iL7G4iIwkVVsYjoMHD0p2e+z1799fjul7v/Q/sps0aQIA2LFjh/EI/cRVW6i57aibN28u\nxyI92aDvh5s8eTIAYM6cOXJM/17c7wIAFi1aBACoU6dORJ/7P9gWEnmJxUVkhG1hGE6cOCF59erV\nkvV9Xq5t6datmxybO3duDEYXv22hu82/V69eckxvxlpY+gzik08+GfBr3O5Z+j6zQmBbSOQlFheR\nkaS/zT8c+nm7HTt2lLxlyxbJbjX9oUOH5Njhw4clx/lml6b0Llrdu3eXXNgbT/WNrcF07dq1UJ8R\nCc5cREYSauZy16H0cplAm8dEg17eE+iJHfoeI70IOJnp+7r0UqhBgwZJfuKJJ0J+v7y8PACB95UE\nfJ+Uoj8vVjhzERlhcREZSah+xa1qv+OOO+SY/ofspEmT/L5HP6pV+/bbbyXr7ZadV199VXKg+8CG\nDBkix2rUqHGloSe0QCcs9CNcdVuoV7I7ekX75s2bJbutw7dv3x7wc/VSJ71rVKxw5iIywuIiMpJQ\ny5/cbfz6Gkp2drbkVq1a+X2PvsU80ofY6fbjV7/6FQDfVidG4nb5U6AlYe5Og1CE86BBfZPq/Pnz\nJbdt2zbkzwsBlz8ReSmhZi7n5MmTkjdu3Ch5yZIlkt2t5+H8rZiZmSm5U6dOkh977DHJHuyh58Tt\nzOXk5ORI1g+y2Llzp+RAt+5f6XfUokULye73CphutcCZi8hLLC4iIwnZFiapuG8Lg3HbAAAF2yPo\n61krV66UrNvCZ555BoDvc9XS0tLMxqmwLSTyEouLyAjbwsRRZNvCIohtIZGXWFxERlhcREZYXERG\nWFxERlhcREZYXERGWFxERlhcREZYXERGWFxERlhcREZYXERGWFxERlhcREZYXERGWFxERlhcREZY\nXERGWFxERlhcREZYXERGWFxERhLqsa2F9dVXX0keMGCAZL3dstOvXz/JnTt3luyeGn/VVVdZDDFh\nfPfdd5L1I3J///vfAwAmTJggx/TemsOHD5d83333AQBatmwpx0qWLBn9wUaIMxeRERYXkZGk385a\nPwn+3nvvlZyXlxfR+73xxhsAgMcff7xQ44pAkdrOetasWZL79Onj93q5cuUk6z+j586d8/ta/bOe\nMmWKZMMnnnA7ayIvsbiIjCR9W3jjjTdK3r9/f6Hfr1KlSgCA5cuXyzF9NstQ3LeFkyZNkjx16lTJ\nx44dk/zyyy8DABo1aiTHjhw5Ilk/fzqQ6tWrS16/fr3k+vXrRzDioNgWEnmJxUVkJOkvIl+8eDGq\n73fixAkAwOTJk+XYO++8E9XPKGqys7MBFLR8gG8r2KNHD8lt2rQB4HuR3j0nGfB9JnLdunUBAKdO\nnZJjOTk5knNzcyVHuS0MCWcuIiNJP3M99dRTkt3Sm1BMmzZNsl6So5dQ0Q9mzJgBwPfaoVsmBgBN\nmzaVfNtttwEA8vPz5didd94pecSIEZIzMzMBAFu2bJFj3bp1k/zHP/7R7zP09TNrnLmIjLC4iIwk\nfVuoWzqdwzF9+nTJbAv9Bbp+6K4HAsCoUaMku5MUeknUmDFjLvv+uoWsWrWq5Llz50ru0qULAKBr\n166hDrvQOHMRGWFxERlJ+rYwHLr90Ge+Dh065MVwiowGDRoAAFauXCnH3nzzTcn16tWTvGLFCgBA\nRkZGyO9//fXXB3zf7t27S54/fz4AtoVECSEhZy79D+jKlStL1v+I3rt3LwDglVdekWNnz56VXLx4\nwd87bnGv+1sVAI4fPy754MGDURh1YtErMPQKCyfQbAWEN2MF0r59+6i9V2Fx5iIywuIiMpKQbaFb\nbgMACxYskFymTBnJX3/9NQDgzJkzJmPQi1GT0cKFCyV/8MEHfq+3aNFCcizat8WLFwMADhw4IMeu\nvfZa08/kzEVkhMVFZCSh2kJ37en999+XY7G+BuWW7+hrLMlCn0HVS8Kc8ePHS3766adjMSThrlF+\n//33MftMzlxERlhcREYSqi10Oy59/PHHno3h6NGjAICNGzfKsdatW3s1nJjSdxXs2bPH73V902Pp\n0qXNx6N3NvNilzPOXERGEmrmigfuH856SU+yzFya3kjmhhtuAABcd911no1B51jhzEVkhMVFZCSh\n2kK30rpEiYL/rUj3JdTbXH/xxRdhf79bdU9Aeno6AKBmzZrmn3X+/HnJ+qF6brlVtWrVzMfgcOYi\nMsLiIjKSUG1hu3btAPg+o3jbtm2X/Z7evXtLHjRokORSpUpJvnDhgt/3/fnPf5b8t7/9TfLu3bvD\nGHFycDeh6ptRrTbnXLZsmWT9u3/ooYcAAOXLlzf53EA4cxEZYXERGUmottBxO/1ES2pqqt8xvWe5\nfrhdhw4dABQsgwJ8z1rFYtlPvNm5cycAYNeuXXKsbdu2UXt/vWeK3vvfa5y5iIwk/WNbo81d09HP\nidJ76enHkbotCPQJmMaNG0f60Z4/tlU/bcRtHw0UzOIPPvigHMvKypIc6UmGc+fOAQD69u0rx+bN\nmydZX9Nyv4Pbb789os/6H3xsK5GXWFxERtgWRtkzzzwDAHjhhRfkWEpKiuQqVapIdjtQzZ49W449\n8sgjkX60522hNm7cOMkTJkzwe123iH/5y18kX+n6l17e5K5R6rZbP+XkrbfekhyldtBhW0jkJRYX\nkZGEvM7lJfcUet2q6L3kXSuY6PTD69xe8Xpp0qJFiyT36tVLcqdOnfzeSy8/e/HFFyW7n2taWpoc\nGzp0qOQot4Jh48xFZIQnNIxMmTJF8siRIwN+zVVXXQUAWLp0qRzr2LFjpB8ZVyc0NDfz6JlEXxO7\nEv1nVN+u71bDjB07Vo5Fc+XHZfCEBpGXWFxERtgWGjl16pTk0aNHS9ZPYHn88ccBAG+88UY0PjJu\n20JHX6N6/fXXJf/617+WfOLECb/vK1mypGS9YNotsWrevHlUxxkCtoVEXmJxERlhW5g44r4tTCBs\nC4m8xOIiMsLiIjLC4iIywuIiMsLiIjLC4iIyEq/3c8X+SWXJiz9rI5y5iIywuIiMsLiIjLC4iIyw\nuIiMsLiIjLC4iIywuIiMsLiIjLC4iIywuIiMsLiIjLC4iIywuIiMsLiIjLC4iIywuIiMsLiIjLC4\niIywuIiMsLiIjLC4iIywuIiMsLiIjLC4iIywuIiM/B8LRNiNqaw8LQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd9d2dda2e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(3, 3 * batch_size))\n",
     "plt.subplot(121)\n",
@@ -7184,22 +4676,7 @@
    "cell_type": "code",
    "execution_count": 175,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [1],\n",
-       "       [0]])"
-      ]
-     },
-     "execution_count": 175,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_batch"
    ]
@@ -7248,134 +4725,7 @@
    "cell_type": "code",
    "execution_count": 177,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Train loss: 0.492426\n",
-      "0 Test accuracy: 0.7861\n",
-      "1 Train loss: 0.334813\n",
-      "2 Train loss: 0.290434\n",
-      "3 Train loss: 0.253434\n",
-      "4 Train loss: 0.217843\n",
-      "5 Train loss: 0.17127\n",
-      "5 Test accuracy: 0.9185\n",
-      "6 Train loss: 0.207128\n",
-      "7 Train loss: 0.172275\n",
-      "8 Train loss: 0.166783\n",
-      "9 Train loss: 0.161094\n",
-      "10 Train loss: 0.125131\n",
-      "10 Test accuracy: 0.9425\n",
-      "11 Train loss: 0.159824\n",
-      "12 Train loss: 0.124752\n",
-      "13 Train loss: 0.112234\n",
-      "14 Train loss: 0.114502\n",
-      "15 Train loss: 0.0950093\n",
-      "15 Test accuracy: 0.9532\n",
-      "16 Train loss: 0.119296\n",
-      "17 Train loss: 0.0754429\n",
-      "18 Train loss: 0.112295\n",
-      "19 Train loss: 0.133708\n",
-      "20 Train loss: 0.113547\n",
-      "20 Test accuracy: 0.9596\n",
-      "21 Train loss: 0.0674082\n",
-      "22 Train loss: 0.0936297\n",
-      "23 Train loss: 0.0986469\n",
-      "24 Train loss: 0.111875\n",
-      "25 Train loss: 0.0735623\n",
-      "25 Test accuracy: 0.9675\n",
-      "26 Train loss: 0.0790324\n",
-      "27 Train loss: 0.0487644\n",
-      "28 Train loss: 0.0869071\n",
-      "29 Train loss: 0.0694422\n",
-      "30 Train loss: 0.060089\n",
-      "30 Test accuracy: 0.9663\n",
-      "31 Train loss: 0.103902\n",
-      "32 Train loss: 0.0535952\n",
-      "33 Train loss: 0.0310679\n",
-      "34 Train loss: 0.0536294\n",
-      "35 Train loss: 0.046265\n",
-      "35 Test accuracy: 0.9701\n",
-      "36 Train loss: 0.0679821\n",
-      "37 Train loss: 0.0326656\n",
-      "38 Train loss: 0.0357479\n",
-      "39 Train loss: 0.0333373\n",
-      "40 Train loss: 0.0415115\n",
-      "40 Test accuracy: 0.9719\n",
-      "41 Train loss: 0.0577977\n",
-      "42 Train loss: 0.0342781\n",
-      "43 Train loss: 0.0439651\n",
-      "44 Train loss: 0.0597254\n",
-      "45 Train loss: 0.0588695\n",
-      "45 Test accuracy: 0.9721\n",
-      "46 Train loss: 0.0556821\n",
-      "47 Train loss: 0.063956\n",
-      "48 Train loss: 0.0301285\n",
-      "49 Train loss: 0.0402678\n",
-      "50 Train loss: 0.0489125\n",
-      "50 Test accuracy: 0.9751\n",
-      "51 Train loss: 0.0394528\n",
-      "52 Train loss: 0.0233041\n",
-      "53 Train loss: 0.064878\n",
-      "54 Train loss: 0.0510189\n",
-      "55 Train loss: 0.0312619\n",
-      "55 Test accuracy: 0.9742\n",
-      "56 Train loss: 0.0244156\n",
-      "57 Train loss: 0.0409082\n",
-      "58 Train loss: 0.0346896\n",
-      "59 Train loss: 0.0455727\n",
-      "60 Train loss: 0.0488268\n",
-      "60 Test accuracy: 0.9751\n",
-      "61 Train loss: 0.0154253\n",
-      "62 Train loss: 0.0358874\n",
-      "63 Train loss: 0.0290555\n",
-      "64 Train loss: 0.0172143\n",
-      "65 Train loss: 0.0377991\n",
-      "65 Test accuracy: 0.9751\n",
-      "66 Train loss: 0.0360786\n",
-      "67 Train loss: 0.0240278\n",
-      "68 Train loss: 0.0314243\n",
-      "69 Train loss: 0.0412082\n",
-      "70 Train loss: 0.0439106\n",
-      "70 Test accuracy: 0.9763\n",
-      "71 Train loss: 0.0169656\n",
-      "72 Train loss: 0.0181306\n",
-      "73 Train loss: 0.0214228\n",
-      "74 Train loss: 0.0418301\n",
-      "75 Train loss: 0.0378622\n",
-      "75 Test accuracy: 0.9759\n",
-      "76 Train loss: 0.0199817\n",
-      "77 Train loss: 0.0145837\n",
-      "78 Train loss: 0.0199176\n",
-      "79 Train loss: 0.0226598\n",
-      "80 Train loss: 0.0119815\n",
-      "80 Test accuracy: 0.9779\n",
-      "81 Train loss: 0.0177832\n",
-      "82 Train loss: 0.00981572\n",
-      "83 Train loss: 0.0279094\n",
-      "84 Train loss: 0.0237818\n",
-      "85 Train loss: 0.0157778\n",
-      "85 Test accuracy: 0.978\n",
-      "86 Train loss: 0.00950592\n",
-      "87 Train loss: 0.0226222\n",
-      "88 Train loss: 0.0226599\n",
-      "89 Train loss: 0.0185005\n",
-      "90 Train loss: 0.0118967\n",
-      "90 Test accuracy: 0.976\n",
-      "91 Train loss: 0.0209059\n",
-      "92 Train loss: 0.0181153\n",
-      "93 Train loss: 0.0131697\n",
-      "94 Train loss: 0.017605\n",
-      "95 Train loss: 0.0193861\n",
-      "95 Test accuracy: 0.976\n",
-      "96 Train loss: 0.0156532\n",
-      "97 Train loss: 0.0136041\n",
-      "98 Train loss: 0.00743028\n",
-      "99 Train loss: 0.0267189\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 100\n",
     "batch_size = 500\n",
@@ -7467,25 +4817,7 @@
    "cell_type": "code",
    "execution_count": 179,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_digit_comparison_model.ckpt\n",
-      "0 Test accuracy: 0.9269\n",
-      "10 Test accuracy: 0.9675\n",
-      "20 Test accuracy: 0.9673\n",
-      "30 Test accuracy: 0.9673\n",
-      "40 Test accuracy: 0.9674\n",
-      "50 Test accuracy: 0.9673\n",
-      "60 Test accuracy: 0.9673\n",
-      "70 Test accuracy: 0.9673\n",
-      "80 Test accuracy: 0.9672\n",
-      "90 Test accuracy: 0.9673\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 100\n",
     "batch_size = 50\n",
@@ -7554,29 +4886,7 @@
    "cell_type": "code",
    "execution_count": 181,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 Test accuracy: 0.8893\n",
-      "10 Test accuracy: 0.9402\n",
-      "20 Test accuracy: 0.9479\n",
-      "30 Test accuracy: 0.9474\n",
-      "40 Test accuracy: 0.9479\n",
-      "50 Test accuracy: 0.9475\n",
-      "60 Test accuracy: 0.9475\n",
-      "70 Test accuracy: 0.9475\n",
-      "80 Test accuracy: 0.9476\n",
-      "90 Test accuracy: 0.9476\n",
-      "100 Test accuracy: 0.9473\n",
-      "110 Test accuracy: 0.9472\n",
-      "120 Test accuracy: 0.9474\n",
-      "130 Test accuracy: 0.9474\n",
-      "140 Test accuracy: 0.9475\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 150\n",
     "batch_size = 50\n",
@@ -7631,7 +4941,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.2"
   },
   "nav_menu": {
    "height": "360px",