diff --git a/11_deep_learning.ipynb b/11_deep_learning.ipynb index c002217..d83e660 100644 --- a/11_deep_learning.ipynb +++ b/11_deep_learning.ipynb @@ -31,9 +31,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# To support both python 2 and python 3\n", @@ -79,9 +77,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def logit(z):\n", @@ -134,9 +130,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf" @@ -145,9 +139,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -161,9 +153,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "he_init = tf.contrib.layers.variance_scaling_initializer()\n", @@ -188,9 +178,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def leaky_relu(z, alpha=0.01):\n", @@ -226,9 +214,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -239,9 +225,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def leaky_relu(z, name=None):\n", @@ -260,9 +244,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -276,9 +258,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\"X\")\n", @@ -288,9 +268,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "with tf.name_scope(\"dnn\"):\n", @@ -302,9 +280,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "with tf.name_scope(\"loss\"):\n", @@ -315,9 +291,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "learning_rate = 0.01\n", @@ -330,9 +304,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "with tf.name_scope(\"eval\"):\n", @@ -343,9 +315,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "init = tf.global_variables_initializer()\n", @@ -404,9 +374,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def elu(z, alpha=1):\n", @@ -441,9 +409,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -454,9 +420,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.elu, name=\"hidden1\")" @@ -479,9 +443,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def selu(z,\n", @@ -543,9 +505,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def selu(z,\n", @@ -571,9 +531,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -668,9 +626,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -702,9 +658,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -723,9 +677,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from functools import partial\n", @@ -753,9 +705,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -811,9 +761,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "n_epochs = 20\n", @@ -912,9 +860,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -946,9 +892,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "learning_rate = 0.01" @@ -964,9 +908,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "threshold = 1.0\n", @@ -988,9 +930,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "with tf.name_scope(\"eval\"):\n", @@ -1001,9 +941,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "init = tf.global_variables_initializer()\n", @@ -1013,9 +951,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "n_epochs = 20\n", @@ -1065,9 +1001,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()" @@ -1076,9 +1010,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "saver = tf.train.import_meta_graph(\"./my_model_final.ckpt.meta\")" @@ -1111,9 +1043,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from IPython.display import clear_output, Image, display, HTML\n", @@ -1175,9 +1105,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "X = tf.get_default_graph().get_tensor_by_name(\"X:0\")\n", @@ -1198,9 +1126,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "for op in (X, y, accuracy, training_op):\n", @@ -1217,9 +1143,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "X, y, accuracy, training_op = tf.get_collection(\"my_important_ops\")" @@ -1280,9 +1204,7 @@ { "cell_type": "code", "execution_count": 55, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -1363,9 +1285,7 @@ { "cell_type": "code", "execution_count": 57, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -1437,9 +1357,7 @@ { "cell_type": "code", "execution_count": 59, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -1489,8 +1407,7 @@ "source": [ "reuse_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\n", " scope=\"hidden[123]\") # regular expression\n", - "reuse_vars_dict = dict([(var.op.name, var) for var in reuse_vars])\n", - "restore_saver = tf.train.Saver(reuse_vars_dict) # to restore layers 1-3\n", + "restore_saver = tf.train.Saver(reuse_vars) # to restore layers 1-3\n", "\n", "init = tf.global_variables_initializer()\n", "saver = tf.train.Saver()\n", @@ -1527,9 +1444,7 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -1670,9 +1585,7 @@ { "cell_type": "code", "execution_count": 67, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -1706,9 +1619,7 @@ { "cell_type": "code", "execution_count": 68, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "with tf.name_scope(\"train\"): # not shown in the book\n", @@ -1721,9 +1632,7 @@ { "cell_type": "code", "execution_count": 69, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "init = tf.global_variables_initializer()\n", @@ -1738,8 +1647,7 @@ "source": [ "reuse_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\n", " scope=\"hidden[123]\") # regular expression\n", - "reuse_vars_dict = dict([(var.op.name, var) for var in reuse_vars])\n", - "restore_saver = tf.train.Saver(reuse_vars_dict) # to restore layers 1-3\n", + "restore_saver = tf.train.Saver(reuse_vars) # to restore layers 1-3\n", "\n", "init = tf.global_variables_initializer()\n", "saver = tf.train.Saver()\n", @@ -1762,9 +1670,7 @@ { "cell_type": "code", "execution_count": 71, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -1783,9 +1689,7 @@ { "cell_type": "code", "execution_count": 72, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "with tf.name_scope(\"dnn\"):\n", @@ -1804,9 +1708,7 @@ { "cell_type": "code", "execution_count": 73, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "with tf.name_scope(\"loss\"):\n", @@ -1837,8 +1739,7 @@ "source": [ "reuse_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\n", " scope=\"hidden[123]\") # regular expression\n", - "reuse_vars_dict = dict([(var.op.name, var) for var in reuse_vars])\n", - "restore_saver = tf.train.Saver(reuse_vars_dict) # to restore layers 1-3\n", + "restore_saver = tf.train.Saver(reuse_vars) # to restore layers 1-3\n", "\n", "init = tf.global_variables_initializer()\n", "saver = tf.train.Saver()\n", @@ -1868,9 +1769,7 @@ { "cell_type": "code", "execution_count": 75, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", @@ -1913,15 +1812,12 @@ { "cell_type": "code", "execution_count": 76, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "reuse_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\n", " scope=\"hidden[123]\") # regular expression\n", - "reuse_vars_dict = dict([(var.op.name, var) for var in reuse_vars])\n", - "restore_saver = tf.train.Saver(reuse_vars_dict) # to restore layers 1-3\n", + "restore_saver = tf.train.Saver(reuse_vars) # to restore layers 1-3\n", "\n", "init = tf.global_variables_initializer()\n", "saver = tf.train.Saver()" @@ -4941,7 +4837,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.4" }, "nav_menu": { "height": "360px",