{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Deep Learning con Keras - Lezione 4 - Addendum.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyM8WmpHlbZgPSwjg9/qGKwr"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["Si tratta di un worflow che avevo preparato per la expainability delle reti neurali basato su MNIST che però non ho poi utilizzato."],"metadata":{"id":"Ih8gtrN4sScY"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"i52hvbIsnkTO","executionInfo":{"status":"ok","timestamp":1648574514039,"user_tz":-120,"elapsed":5022,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}}},"outputs":[],"source":["import numpy as np\n","import tensorflow as tf\n","import matplotlib.pyplot as plt"]},{"cell_type":"markdown","source":["# Preparazione dati di MNIST"],"metadata":{"id":"Y1ZZvgWsmCvh"}},{"cell_type":"code","source":["# Carichiamo il dataset MNIST\n","# - train_images sono le immagini per la fase di addestramento\n","# - train_labels sono le etichette associate a queste immagini (numeri da 0 a 9) \n","\n","# test_images e test_labels sono simili a train_images e train_labels ma costituiscono \n","# i dati per la fase di test (ne parleremo dopo)\n","\n","(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()"],"metadata":{"id":"R_8lwtovTtrE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648574519889,"user_tz":-120,"elapsed":1149,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"1fb76503-1501-4378-8190-06483b10ad2d"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","11493376/11490434 [==============================] - 0s 0us/step\n","11501568/11490434 [==============================] - 0s 0us/step\n"]}]},{"cell_type":"code","source":["# Occorre trasformare le etichette dal formato numerico originale ad un formato\n","# confrontabile con l'output del percettrone. Usiamo il metodo predefinito di\n","# tf.keras.utils.to_categorical.\n","\n","train_labels_ok = tf.keras.utils.to_categorical(train_labels)\n","test_labels_ok = tf.keras.utils.to_categorical(test_labels)"],"metadata":{"id":"UGt-pP9DgCyY","executionInfo":{"status":"ok","timestamp":1648574521380,"user_tz":-120,"elapsed":16,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["# È prassi fornire i dati in input in maniera normalizzata, come numeri compresi\n","# tra 0 ed 1. Pertanto, anche se non strettamente necessario, dividiamo tutti i\n","# numeri che compongono una immagine per 255.\n","\n","train_images_ok = train_images / 255.0\n","test_images_ok = test_images / 255"],"metadata":{"id":"PU7lzXaZgxzI","executionInfo":{"status":"ok","timestamp":1648574523211,"user_tz":-120,"elapsed":371,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["# La prima rete\n","\n","Riprendiamo qui la rete neural simile al percettrone che abbiamo presentato alla fine della 1° lezione."],"metadata":{"id":"AoMYAAdaBqDp"}},{"cell_type":"code","source":["network = tf.keras.models.Sequential([\n","  tf.keras.layers.Flatten(input_shape=(28, 28)),\n","  tf.keras.layers.Dense(10, activation='hard_sigmoid')\n","])\n","network.summary()"],"metadata":{"id":"ijqRtHB-dbLc","executionInfo":{"status":"ok","timestamp":1648574557096,"user_tz":-120,"elapsed":361,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"8553e039-9854-45f4-9e20-a11ce99d9918"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," flatten_1 (Flatten)         (None, 784)               0         \n","                                                                 \n"," dense_1 (Dense)             (None, 10)                7850      \n","                                                                 \n","=================================================================\n","Total params: 7,850\n","Trainable params: 7,850\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["network.compile(optimizer='adam', loss='mse', metrics=['accuracy'])\n","history = network.fit(train_images_ok, train_labels_ok, epochs=15, batch_size=128)"],"metadata":{"id":"ldsTGQkmef5M","executionInfo":{"status":"ok","timestamp":1648574580518,"user_tz":-120,"elapsed":21973,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"2887fcc6-b324-4de1-f460-698b9fa7680c"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/15\n","469/469 [==============================] - 2s 3ms/step - loss: 0.0517 - accuracy: 0.7379\n","Epoch 2/15\n","469/469 [==============================] - 1s 3ms/step - loss: 0.0261 - accuracy: 0.8765\n","Epoch 3/15\n","469/469 [==============================] - 1s 3ms/step - loss: 0.0223 - accuracy: 0.8909\n","Epoch 4/15\n","469/469 [==============================] - 1s 2ms/step - loss: 0.0204 - accuracy: 0.8975\n","Epoch 5/15\n","469/469 [==============================] - 1s 2ms/step - loss: 0.0193 - accuracy: 0.9016\n","Epoch 6/15\n","469/469 [==============================] - 1s 3ms/step - loss: 0.0186 - accuracy: 0.9047\n","Epoch 7/15\n","469/469 [==============================] - 1s 3ms/step - loss: 0.0180 - accuracy: 0.9071\n","Epoch 8/15\n","469/469 [==============================] - 1s 2ms/step - loss: 0.0175 - accuracy: 0.9090\n","Epoch 9/15\n","469/469 [==============================] - 1s 2ms/step - loss: 0.0171 - accuracy: 0.9108\n","Epoch 10/15\n","469/469 [==============================] - 1s 3ms/step - loss: 0.0168 - accuracy: 0.9118\n","Epoch 11/15\n","469/469 [==============================] - 1s 2ms/step - loss: 0.0165 - accuracy: 0.9135\n","Epoch 12/15\n","469/469 [==============================] - 1s 3ms/step - loss: 0.0163 - accuracy: 0.9144\n","Epoch 13/15\n","469/469 [==============================] - 1s 3ms/step - loss: 0.0161 - accuracy: 0.9158\n","Epoch 14/15\n","469/469 [==============================] - 1s 3ms/step - loss: 0.0159 - accuracy: 0.9165\n","Epoch 15/15\n","469/469 [==============================] - 1s 3ms/step - loss: 0.0157 - accuracy: 0.9172\n"]}]},{"cell_type":"code","source":["network.evaluate(test_images_ok, test_labels_ok)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1DMEhGsBN9Yn","executionInfo":{"status":"ok","timestamp":1648574581098,"user_tz":-120,"elapsed":601,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"e45383fd-9aa4-41db-a2c9-0af9cc433f4d"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["313/313 [==============================] - 1s 1ms/step - loss: 0.0154 - accuracy: 0.9191\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.015406408347189426, 0.9190999865531921]"]},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","source":["# Visualizzazione pesi"],"metadata":{"id":"A5nvbiTeKeQT"}},{"cell_type":"code","source":["# Determino i pesi della rete.\n","\n","weights=network.layers[-1].get_weights()"],"metadata":{"id":"a4R6QJL9Kj8v","executionInfo":{"status":"ok","timestamp":1648574608871,"user_tz":-120,"elapsed":334,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["# weights[1] è un elenco di 10 numeri (il bias dei 10 neuroni)\n","\n","weights[1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0VvE0KU1LyO6","executionInfo":{"status":"ok","timestamp":1648574615958,"user_tz":-120,"elapsed":573,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"b2b6a0ff-237c-4776-bc3b-589026cb1094"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([-1.0304863e+00, -3.2227093e-01, -8.4367031e-01, -1.2860960e+00,\n","       -6.5709859e-01,  1.8845893e-03, -1.0437469e+00, -2.7709302e-01,\n","       -2.4211462e+00, -1.4106199e+00], dtype=float32)"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["# weights[0] è un tensore di 7840 pesi, 784 per ogni neurone\n","\n","weights[0].shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I2rk2vq9L4cs","executionInfo":{"status":"ok","timestamp":1648574618303,"user_tz":-120,"elapsed":361,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"5cf409a7-4ebc-447f-a4ee-ac9fd3944e56"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(784, 10)"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["import matplotlib as mpl\n","\n","# Extraggo i pesi associati al neurone 0 e li vedo come un'immagine bidimensionale \n","# (faccio cioè l'inverso del layer Flatten)\n","#\n","# Per il significato dei colori, consultare l'elenco delle colormap su\n","# https://matplotlib.org/3.5.1/tutorials/colors/colormaps.html.\n","\n","# Per la colormap coolwarm, il blu indica un valore negativo, il rosso uno positivo.\n","# A colore più vivo corrisponde un valore assoluto superiore.\n","\n","\n","w = weights[0][:,0]\n","maxw = np.max(np.abs(weights[0]))\n","plt.imshow(w.reshape(28,28), norm = mpl.colors.Normalize(vmin=-maxw/2, vmax=maxw/2), cmap = plt.cm.coolwarm)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"UGEBINrQMKcU","executionInfo":{"status":"ok","timestamp":1648575621067,"user_tz":-120,"elapsed":27,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"45e98716-e9fe-4c1e-9e13-ff20d81b0fc8"},"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fa6a3660850>"]},"metadata":{},"execution_count":22},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Visualizzare i filtri di una rete convoluzionale"],"metadata":{"id":"iyHP58VCglWk"}},{"cell_type":"code","source":["# Riadattiamo le immagini di training in modo che ogni immagine sia un tensore\n","# tridimensionale (operazione richiesta per dare in put le immagini ad un\n","# layer convoluzionale)\n","\n","x_train = train_images_ok.reshape((60000, 28, 28, 1))\n","x_test = test_images_ok.reshape((10000, 28, 28, 1))"],"metadata":{"id":"ucqV5Zz0PB0g","executionInfo":{"status":"ok","timestamp":1648575079454,"user_tz":-120,"elapsed":17,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["# Questa è la rete convoluzionale\n","\n","network2 = tf.keras.models.Sequential([\n","  tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n","  tf.keras.layers.MaxPooling2D((2, 2)),\n","  tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n","  tf.keras.layers.MaxPooling2D((2, 2)),\n","  tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n","  tf.keras.layers.Flatten(),\n","  tf.keras.layers.Dense(64, activation='relu'),\n","  tf.keras.layers.Dense(10, activation='softmax')\n","])\n","network2.summary()"],"metadata":{"id":"-7xFYZWCLJo-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648575093749,"user_tz":-120,"elapsed":389,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"027134af-5f86-4554-956b-890bea58455d"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 26, 26, 32)        320       \n","                                                                 \n"," max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n"," )                                                               \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n","                                                                 \n"," max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n"," 2D)                                                             \n","                                                                 \n"," conv2d_2 (Conv2D)           (None, 3, 3, 64)          36928     \n","                                                                 \n"," flatten_2 (Flatten)         (None, 576)               0         \n","                                                                 \n"," dense_2 (Dense)             (None, 64)                36928     \n","                                                                 \n"," dense_3 (Dense)             (None, 10)                650       \n","                                                                 \n","=================================================================\n","Total params: 93,322\n","Trainable params: 93,322\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["# Addestriamo la rete\n","\n","network2.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n","network2.fit(x_train, train_labels_ok, epochs=10, batch_size=128)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MvLb5-ruO4n0","executionInfo":{"status":"ok","timestamp":1648575620120,"user_tz":-120,"elapsed":502724,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"be07cfff-ff8b-453e-cfd8-94e35244214c"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","469/469 [==============================] - 56s 118ms/step - loss: 0.1200 - accuracy: 0.9634\n","Epoch 2/10\n","469/469 [==============================] - 56s 118ms/step - loss: 0.0515 - accuracy: 0.9839\n","Epoch 3/10\n","469/469 [==============================] - 53s 113ms/step - loss: 0.0377 - accuracy: 0.9882\n","Epoch 4/10\n","469/469 [==============================] - 47s 100ms/step - loss: 0.0299 - accuracy: 0.9902\n","Epoch 5/10\n","469/469 [==============================] - 47s 100ms/step - loss: 0.0239 - accuracy: 0.9926\n","Epoch 6/10\n","469/469 [==============================] - 47s 101ms/step - loss: 0.0197 - accuracy: 0.9932\n","Epoch 7/10\n","469/469 [==============================] - 46s 99ms/step - loss: 0.0154 - accuracy: 0.9952\n","Epoch 8/10\n","469/469 [==============================] - 47s 99ms/step - loss: 0.0138 - accuracy: 0.9952\n","Epoch 9/10\n","469/469 [==============================] - 47s 100ms/step - loss: 0.0119 - accuracy: 0.9960\n","Epoch 10/10\n","469/469 [==============================] - 47s 101ms/step - loss: 0.0108 - accuracy: 0.9965\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7fa6a37caf90>"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["# Determiniamo i pesi dei neuroni del 1° strato convoluzionale. Lo shape di questi\n","# pesi è 3 * 3 * 1 * 32. Dove:\n","# - 32 è il numero di filtri nel primo strato convoluzionale\n","# - 3 * 3 * 1è la dimensione del filtro \n","# - 1 è il numero di canali della immagini di input (sarebbe 3 se fosse a colori)\n","\n","weights = network2.layers[0].weights[0]\n","weights.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QlNwXbqUO8Nx","executionInfo":{"status":"ok","timestamp":1648575632562,"user_tz":-120,"elapsed":317,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"96cd0b5a-4383-413b-b04f-af919ed06bd1"},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["TensorShape([3, 3, 1, 32])"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["# Il filtro 27 sembra enfatizzare le line diagonali (da basso sx ad alto dx). Notare\n","# che riaddestrando la rete questi valori cambiano.\n","\n","maxw = np.max(np.abs(weights))\n","plt.imshow(weights[:,:,0,27], norm = mpl.colors.Normalize(vmin=-maxw, vmax=maxw), cmap = plt.cm.coolwarm)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"EqN9aMhIowd9","executionInfo":{"status":"ok","timestamp":1648575806353,"user_tz":-120,"elapsed":322,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"46b01695-1b05-4f2d-82eb-2c52eb7b5fff"},"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fa69d9a5ad0>"]},"metadata":{},"execution_count":32},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# Questi sono tutti i 32 filtri\n","\n","for i in range(32):\n","  plt.title(f\"Filtro {i}\")\n","  plt.imshow(weights[:,:,0, i], norm = mpl.colors.Normalize(vmin=-maxw, vmax=maxw), cmap=plt.cm.coolwarm)\n","  plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"F0KBtJRqrOYy","executionInfo":{"status":"ok","timestamp":1648575689416,"user_tz":-120,"elapsed":5146,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"79d6e6e2-f345-4b70-8f31-f89ef8e77382"},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Visualizzare gli output degli strati intermedi"],"metadata":{"id":"KsCDuxyMUt-r"}},{"cell_type":"code","source":["# Genero un modello con i primi 5 layer e calcolo le attivazioni generate dalla\n","# immagine numero 0\n","\n","layer_outputs = [layer.output for layer in network2.layers[:5]]\n","activation_model = tf.keras.models.Model(inputs=network2.input, outputs=layer_outputs)\n","activations = activation_model.predict(x_train[0:1,])"],"metadata":{"id":"GpA7zTI4Uxlm","executionInfo":{"status":"ok","timestamp":1648575846662,"user_tz":-120,"elapsed":345,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}}},"execution_count":34,"outputs":[]},{"cell_type":"code","source":["# Questa è l'immagine numero 0\n","\n","plt.imshow(train_images[0], cmap = plt.cm.binary)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"GTFaENz0U811","executionInfo":{"status":"ok","timestamp":1648576081069,"user_tz":-120,"elapsed":507,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"e09247d0-d90d-4737-ca73-c80854fddb56"},"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fa69daca690>"]},"metadata":{},"execution_count":41},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# Questa è l'attivazione del canale 27. Notare come le righe diagonali nella\n","# direzione da basso sinistra ad alto destra sono enfatizzate, come intuibile dal\n","# filtro corrisponsente.\n","\n","plt.imshow(activations[0][0, :, :, 27])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"_52LHBygWZh5","executionInfo":{"status":"ok","timestamp":1648576094184,"user_tz":-120,"elapsed":522,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"e2722e8c-26b4-40e2-e941-cee6b2563a46"},"execution_count":43,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fa69dbc9f50>"]},"metadata":{},"execution_count":43},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# Questo codice visualizza tutte le attivazioni di tutti gli strati convoluzionali.\n","# Notare che i primi strati generano attivazioni che sono alterazioni dell'immagine\n","# di partenza, mentre gli strati più avanzati hanno meno corrispondenza con l'immagine\n","# iniziale.\n","\n","def plot_all_activations(activation_model, img):\n","  activations = activation_model.predict(img)\n","  layer_names = []\n","  for layer in activation_model.outputs:\n","      layer_names.append(layer.name)\n","  images_per_row = 16\n","\n","  for layer_name, layer_activation in zip(layer_names, activations):\n","    n_features = layer_activation.shape[-1]\n","    size = layer_activation.shape[1]\n","    n_cols = n_features // images_per_row\n","    display_grid = np.zeros((size * n_cols, images_per_row * size))\n","    for col in range(n_cols):\n","      for row in range(images_per_row):\n","        channel_image = layer_activation[0,:,:,col*images_per_row + row]\n","        channel_image -= channel_image.mean()\n","        channel_image /= channel_image.std()\n","        channel_image *= 64\n","        channel_image += 128\n","        channel_image = np.clip(channel_image, 0, 255).astype('uint8')\n","        display_grid[col * size : (col + 1) * size, row * size : (row + 1) * size] = channel_image\n","    scale = 1. / size\n","    plt.figure(figsize=(scale * display_grid.shape[1], scale * display_grid.shape[0]))\n","    plt.title(layer_name)\n","    plt.grid(False)\n","    plt.imshow(display_grid, aspect='auto', cmap='viridis')\n"],"metadata":{"id":"vKXX9eTzWpwu","executionInfo":{"status":"ok","timestamp":1648576204273,"user_tz":-120,"elapsed":325,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}}},"execution_count":44,"outputs":[]},{"cell_type":"code","source":["plot_all_activations(activation_model, x_train[0:1,])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"EVBuJ_CtzJQP","executionInfo":{"status":"ok","timestamp":1648576253044,"user_tz":-120,"elapsed":1890,"user":{"displayName":"Gianluca Amato","userId":"18269286707108730791"}},"outputId":"998dedff-211a-4465-9f62-8f0dd08f423b"},"execution_count":48,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in true_divide\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1152x144 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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ggk1GVf1akq8lOS///b6g/5Wp93na15kIVfXLmTqxyrxM/fH+I62111XVAzN1UsTtknwnyfNaa7fM3aQwc1X1G0le2Vp7mn2cHsaqeAIAADB5xumltgAAAEwgxRMAAIBBKZ4AAAAMSvEEAABgUIonAAAAg1I8AQAAGJTiCQAAwKD+P0hdjEJdQ/HzAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[""],"metadata":{"id":"FI8GPINazHkq"},"execution_count":null,"outputs":[]}]}