diff --git a/12_custom_models_and_training_with_tensorflow.ipynb b/12_custom_models_and_training_with_tensorflow.ipynb index 3dec28f..be59c78 100644 --- a/12_custom_models_and_training_with_tensorflow.ipynb +++ b/12_custom_models_and_training_with_tensorflow.ipynb @@ -14,6 +14,17 @@ "_This notebook contains all the sample code in chapter 12._" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \n", + "
\n", + " Run in Google Colab\n", + "
" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -25,7 +36,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview." + "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0." ] }, { @@ -42,7 +53,13 @@ "import sklearn\n", "assert sklearn.__version__ >= \"0.20\"\n", "\n", - "# TensorFlow ≥2.0-preview is required\n", + "try:\n", + " # %tensorflow_version only exists in Colab.\n", + " %tensorflow_version 2.x\n", + "except Exception:\n", + " pass\n", + "\n", + "# TensorFlow ≥2.0 is required\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "assert tf.__version__ >= \"2.0\"\n", @@ -1141,17 +1158,14 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO: \n", - "\"\"\"\n", "model = keras.models.load_model(\n", " \"my_model_with_many_custom_parts.h5\",\n", " custom_objects={\n", - " \"my_l1_regularizer\": my_l1_regularizer(0.01),\n", - " \"my_positive_weights\": my_positive_weights,\n", + " \"my_l1_regularizer\": my_l1_regularizer,\n", + " \"my_positive_weights\": lambda: my_positive_weights,\n", " \"my_glorot_initializer\": my_glorot_initializer,\n", " \"my_softplus\": my_softplus,\n", - " })\n", - "\"\"\"" + " })" ] }, { @@ -1219,17 +1233,14 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO: check https://github.com/tensorflow/tensorflow/issues/26061\n", - "\"\"\"\n", "model = keras.models.load_model(\n", " \"my_model_with_many_custom_parts.h5\",\n", " custom_objects={\n", " \"MyL1Regularizer\": MyL1Regularizer,\n", - " \"my_positive_weights\": my_positive_weights,\n", + " \"my_positive_weights\": lambda: my_positive_weights,\n", " \"my_glorot_initializer\": my_glorot_initializer,\n", " \"my_softplus\": my_softplus,\n", - " })\n", - "\"\"\"" + " })" ] }, { @@ -1379,9 +1390,15 @@ " def __init__(self, threshold=1.0, **kwargs):\n", " super().__init__(**kwargs) # handles base args (e.g., dtype)\n", " self.threshold = threshold\n", - " self.huber_fn = create_huber(threshold)\n", + " #self.huber_fn = create_huber(threshold) # TODO: investigate why this fails\n", " self.total = self.add_weight(\"total\", initializer=\"zeros\")\n", " self.count = self.add_weight(\"count\", initializer=\"zeros\")\n", + " def huber_fn(self, y_true, y_pred): # workaround\n", + " error = y_true - y_pred\n", + " is_small_error = tf.abs(error) < self.threshold\n", + " squared_loss = tf.square(error) / 2\n", + " linear_loss = self.threshold * tf.abs(error) - self.threshold**2 / 2\n", + " return tf.where(is_small_error, squared_loss, linear_loss)\n", " def update_state(self, y_true, y_pred, sample_weight=None):\n", " metric = self.huber_fn(y_true, y_pred)\n", " self.total.assign_add(tf.reduce_sum(metric))\n", @@ -1475,7 +1492,7 @@ "metadata": {}, "outputs": [], "source": [ - "model.fit(X_train_scaled, y_train, epochs=2)" + "model.fit(X_train_scaled.astype(np.float32), y_train.astype(np.float32), epochs=2)" ] }, { @@ -1504,7 +1521,7 @@ "metadata": {}, "outputs": [], "source": [ - "model.fit(X_train_scaled, y_train, epochs=2)" + "model.fit(X_train_scaled.astype(np.float32), y_train.astype(np.float32), epochs=2)" ] }, { @@ -1862,10 +1879,10 @@ " Z = layer(Z)\n", " return inputs + Z\n", " \n", - " def get_config(self): # not shown\n", - " base_config = super().get_config() # not shown\n", - " return {**base_config, # not shown\n", - " \"n_layers\": self.n_layers, \"n_neurons\": n_neurons} # not shown" + " def get_config(self): # not shown\n", + " base_config = super().get_config() # not shown\n", + " return {**base_config, # not shown\n", + " \"n_layers\": self.n_layers, \"n_neurons\": self.n_neurons} # not shown" ] }, { @@ -1916,13 +1933,25 @@ "metadata": {}, "outputs": [], "source": [ - "#TODO: check that persistence ends up working in TF2\n", - "#model.save(\"my_custom_model.h5\")\n", - "#model = keras.models.load_model(\"my_custom_model.h5\",\n", - "# custom_objects={\n", - "# \"ResidualBlock\": ResidualBlock,\n", - "# \"ResidualRegressor\": ResidualRegressor\n", - "# })" + "model.save(\"my_custom_model.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "model = keras.models.load_model(\"my_custom_model.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [], + "source": [ + "history = model.fit(X_train_scaled, y_train, epochs=5)" ] }, { @@ -1934,7 +1963,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 148, "metadata": {}, "outputs": [], "source": [ @@ -1949,7 +1978,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 149, "metadata": {}, "outputs": [], "source": [ @@ -1967,11 +1996,11 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 150, "metadata": {}, + "outputs": [], "source": [ - "TODO: check https://github.com/tensorflow/tensorflow/issues/26260\n", - "```python\n", "class ReconstructingRegressor(keras.models.Model):\n", " def __init__(self, output_dim, **kwargs):\n", " super().__init__(**kwargs)\n", @@ -1979,70 +2008,34 @@ " kernel_initializer=\"lecun_normal\")\n", " for _ in range(5)]\n", " self.out = keras.layers.Dense(output_dim)\n", - " self.reconstruction_mean = keras.metrics.Mean(name=\"reconstruction_error\")\n", + " # TODO: check https://github.com/tensorflow/tensorflow/issues/26260\n", + " #self.reconstruction_mean = keras.metrics.Mean(name=\"reconstruction_error\")\n", "\n", " def build(self, batch_input_shape):\n", " n_inputs = batch_input_shape[-1]\n", " self.reconstruct = keras.layers.Dense(n_inputs)\n", " super().build(batch_input_shape)\n", "\n", - " @tf.function\n", " def call(self, inputs, training=None):\n", " Z = inputs\n", " for layer in self.hidden:\n", " Z = layer(Z)\n", " reconstruction = self.reconstruct(Z)\n", " recon_loss = tf.reduce_mean(tf.square(reconstruction - inputs))\n", - " self.add_loss(0.05 * reconstruction_loss)\n", - " if training:\n", - " result = self.reconstruction_mean(recon_loss)\n", - " self.add_metric(result)\n", - " return self.out(Z)\n", - "\n", - "model = ReconstructingRegressor(1)\n", - "model.build(tf.TensorShape([None, 8])) # <= Fails if this line is removed\n", - "model.compile(loss=\"mse\", optimizer=\"nadam\")\n", - "history = model.fit(X, y, epochs=2)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "metadata": {}, - "outputs": [], - "source": [ - "class ReconstructingRegressor(keras.models.Model):\n", - " def __init__(self, output_dim, **kwargs):\n", - " super().__init__(**kwargs)\n", - " self.hidden = [keras.layers.Dense(30, activation=\"selu\",\n", - " kernel_initializer=\"lecun_normal\")\n", - " for _ in range(5)]\n", - " self.out = keras.layers.Dense(output_dim)\n", - "\n", - " def build(self, batch_input_shape):\n", - " n_inputs = batch_input_shape[-1]\n", - " self.reconstruct = keras.layers.Dense(n_inputs)\n", - " super().build(batch_input_shape)\n", - "\n", - " def call(self, inputs):\n", - " Z = inputs\n", - " for layer in self.hidden:\n", - " Z = layer(Z)\n", - " reconstruction = self.reconstruct(Z)\n", - " recon_loss = tf.reduce_mean(tf.square(reconstruction - inputs))\n", " self.add_loss(0.05 * recon_loss)\n", + " #if training:\n", + " # result = self.reconstruction_mean(recon_loss)\n", + " # self.add_metric(result)\n", " return self.out(Z)" ] }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 151, "metadata": {}, "outputs": [], "source": [ "model = ReconstructingRegressor(1)\n", - "model.build(tf.TensorShape([None, 8])) # TODO: check https://github.com/tensorflow/tensorflow/issues/26274\n", "model.compile(loss=\"mse\", optimizer=\"nadam\")\n", "history = model.fit(X_train_scaled, y_train, epochs=2)\n", "y_pred = model.predict(X_test_scaled)" @@ -2057,7 +2050,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 152, "metadata": {}, "outputs": [], "source": [ @@ -2067,7 +2060,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 153, "metadata": {}, "outputs": [], "source": [ @@ -2078,7 +2071,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 154, "metadata": {}, "outputs": [], "source": [ @@ -2087,7 +2080,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 155, "metadata": {}, "outputs": [], "source": [ @@ -2100,7 +2093,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 156, "metadata": {}, "outputs": [], "source": [ @@ -2109,7 +2102,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 157, "metadata": {}, "outputs": [], "source": [ @@ -2125,7 +2118,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 158, "metadata": {}, "outputs": [], "source": [ @@ -2139,7 +2132,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 159, "metadata": {}, "outputs": [], "source": [ @@ -2148,7 +2141,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 160, "metadata": {}, "outputs": [], "source": [ @@ -2159,29 +2152,6 @@ "gradients = tape.gradient(z, [c1, c2])" ] }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": {}, - "outputs": [], - "source": [ - "gradients" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": {}, - "outputs": [], - "source": [ - "with tf.GradientTape() as tape:\n", - " tape.watch(c1)\n", - " tape.watch(c2)\n", - " z = f(c1, c2)\n", - "\n", - "gradients = tape.gradient(z, [c1, c2])" - ] - }, { "cell_type": "code", "execution_count": 161, @@ -2196,6 +2166,29 @@ "execution_count": 162, "metadata": {}, "outputs": [], + "source": [ + "with tf.GradientTape() as tape:\n", + " tape.watch(c1)\n", + " tape.watch(c2)\n", + " z = f(c1, c2)\n", + "\n", + "gradients = tape.gradient(z, [c1, c2])" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [], + "source": [ + "gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [], "source": [ "with tf.GradientTape() as tape:\n", " z1 = f(w1, w2 + 2.)\n", @@ -2207,7 +2200,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 165, "metadata": {}, "outputs": [], "source": [ @@ -2222,7 +2215,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 166, "metadata": {}, "outputs": [], "source": [ @@ -2237,7 +2230,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 167, "metadata": {}, "outputs": [], "source": [ @@ -2246,7 +2239,7 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 168, "metadata": {}, "outputs": [], "source": [ @@ -2255,7 +2248,7 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 169, "metadata": {}, "outputs": [], "source": [ @@ -2270,7 +2263,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 170, "metadata": {}, "outputs": [], "source": [ @@ -2283,7 +2276,7 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 171, "metadata": {}, "outputs": [], "source": [ @@ -2292,7 +2285,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 172, "metadata": {}, "outputs": [], "source": [ @@ -2305,7 +2298,7 @@ }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 173, "metadata": {}, "outputs": [], "source": [ @@ -2319,7 +2312,7 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 174, "metadata": {}, "outputs": [], "source": [ @@ -2329,7 +2322,7 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 175, "metadata": {}, "outputs": [], "source": [ @@ -2349,7 +2342,7 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 176, "metadata": {}, "outputs": [], "source": [ @@ -2363,7 +2356,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 177, "metadata": {}, "outputs": [], "source": [ @@ -2374,7 +2367,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 178, "metadata": {}, "outputs": [], "source": [ @@ -2388,7 +2381,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 179, "metadata": {}, "outputs": [], "source": [ @@ -2413,7 +2406,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 180, "metadata": {}, "outputs": [], "source": [ @@ -2428,7 +2421,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 181, "metadata": {}, "outputs": [], "source": [ @@ -2437,7 +2430,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 182, "metadata": {}, "outputs": [], "source": [ @@ -2450,7 +2443,7 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 183, "metadata": {}, "outputs": [], "source": [ @@ -2466,7 +2459,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 184, "metadata": {}, "outputs": [], "source": [ @@ -2481,7 +2474,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 185, "metadata": {}, "outputs": [], "source": [ @@ -2509,7 +2502,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 186, "metadata": {}, "outputs": [], "source": [ @@ -2552,7 +2545,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 187, "metadata": {}, "outputs": [], "source": [ @@ -2562,7 +2555,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 188, "metadata": {}, "outputs": [], "source": [ @@ -2571,7 +2564,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 189, "metadata": {}, "outputs": [], "source": [ @@ -2580,7 +2573,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 190, "metadata": {}, "outputs": [], "source": [ @@ -2590,7 +2583,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 191, "metadata": {}, "outputs": [], "source": [ @@ -2599,7 +2592,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 192, "metadata": {}, "outputs": [], "source": [ @@ -2615,7 +2608,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 193, "metadata": {}, "outputs": [], "source": [ @@ -2625,7 +2618,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 194, "metadata": {}, "outputs": [], "source": [ @@ -2634,7 +2627,7 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 195, "metadata": {}, "outputs": [], "source": [ @@ -2650,7 +2643,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 196, "metadata": {}, "outputs": [], "source": [ @@ -2659,7 +2652,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 197, "metadata": {}, "outputs": [], "source": [ @@ -2669,7 +2662,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 198, "metadata": {}, "outputs": [], "source": [ @@ -2679,7 +2672,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 199, "metadata": {}, "outputs": [], "source": [ @@ -2688,7 +2681,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 200, "metadata": {}, "outputs": [], "source": [ @@ -2697,7 +2690,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 201, "metadata": {}, "outputs": [], "source": [ @@ -2706,7 +2699,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 202, "metadata": {}, "outputs": [], "source": [ @@ -2722,7 +2715,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 203, "metadata": {}, "outputs": [], "source": [ @@ -2734,7 +2727,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 204, "metadata": {}, "outputs": [], "source": [ @@ -2743,7 +2736,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 205, "metadata": {}, "outputs": [], "source": [ @@ -2752,7 +2745,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 206, "metadata": {}, "outputs": [], "source": [ @@ -2772,7 +2765,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 207, "metadata": {}, "outputs": [], "source": [ @@ -2784,7 +2777,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 208, "metadata": {}, "outputs": [], "source": [ @@ -2796,7 +2789,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 209, "metadata": {}, "outputs": [], "source": [ @@ -2823,7 +2816,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 210, "metadata": {}, "outputs": [], "source": [ @@ -2836,7 +2829,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 211, "metadata": {}, "outputs": [], "source": [ @@ -2845,7 +2838,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 212, "metadata": {}, "outputs": [], "source": [ @@ -2861,7 +2854,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 213, "metadata": {}, "outputs": [], "source": [ @@ -2875,7 +2868,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 214, "metadata": {}, "outputs": [], "source": [ @@ -2884,7 +2877,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 215, "metadata": {}, "outputs": [], "source": [ @@ -2900,7 +2893,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 216, "metadata": {}, "outputs": [], "source": [ @@ -2913,7 +2906,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 217, "metadata": {}, "outputs": [], "source": [ @@ -2929,7 +2922,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 218, "metadata": {}, "outputs": [], "source": [ @@ -2942,7 +2935,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 219, "metadata": {}, "outputs": [], "source": [ @@ -2952,7 +2945,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 220, "metadata": {}, "outputs": [], "source": [ @@ -2962,7 +2955,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 221, "metadata": {}, "outputs": [], "source": [ @@ -2975,7 +2968,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 222, "metadata": {}, "outputs": [], "source": [ @@ -2985,7 +2978,7 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 223, "metadata": {}, "outputs": [], "source": [ @@ -2995,7 +2988,7 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 224, "metadata": {}, "outputs": [], "source": [ @@ -3010,7 +3003,7 @@ }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 225, "metadata": {}, "outputs": [], "source": [ @@ -3021,7 +3014,7 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 226, "metadata": { "scrolled": true }, @@ -3033,28 +3026,26 @@ " x += 1\n", " return x\n", "\n", - "tf.autograph.to_code(add_10.python_function, experimental_optional_features=None)\n", - "# TODO: experimental_optional_features is needed to have the same behavior as @tf.function,\n", - "# check that this is not needed when TF2 is released" + "tf.autograph.to_code(add_10.python_function)" ] }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 227, "metadata": {}, "outputs": [], "source": [ - "def display_tf_code(func, experimental_optional_features=None):\n", + "def display_tf_code(func):\n", " from IPython.display import display, Markdown\n", " if hasattr(func, \"python_function\"):\n", " func = func.python_function\n", - " code = tf.autograph.to_code(func, experimental_optional_features=experimental_optional_features)\n", + " code = tf.autograph.to_code(func)\n", " display(Markdown('```python\\n{}\\n```'.format(code)))" ] }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 228, "metadata": {}, "outputs": [], "source": [ @@ -3078,7 +3069,7 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 229, "metadata": {}, "outputs": [], "source": [ @@ -3090,7 +3081,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 230, "metadata": {}, "outputs": [], "source": [ @@ -3102,7 +3093,7 @@ }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 231, "metadata": {}, "outputs": [], "source": [ @@ -3131,7 +3122,7 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 232, "metadata": {}, "outputs": [], "source": [ @@ -3156,7 +3147,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 233, "metadata": {}, "outputs": [], "source": [ @@ -3165,7 +3156,7 @@ }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 234, "metadata": {}, "outputs": [], "source": [ @@ -3183,7 +3174,7 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 235, "metadata": {}, "outputs": [], "source": [ @@ -3192,7 +3183,7 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 236, "metadata": {}, "outputs": [], "source": [ @@ -3208,7 +3199,7 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 237, "metadata": {}, "outputs": [], "source": [ @@ -3226,7 +3217,7 @@ }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 238, "metadata": {}, "outputs": [], "source": [ @@ -3235,7 +3226,7 @@ }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 239, "metadata": {}, "outputs": [], "source": [ @@ -3244,7 +3235,7 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 240, "metadata": {}, "outputs": [], "source": [ @@ -3269,7 +3260,7 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 241, "metadata": {}, "outputs": [], "source": [ @@ -3315,7 +3306,7 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 242, "metadata": { "scrolled": false }, @@ -3343,7 +3334,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.7.3" } }, "nbformat": 4,