2-3-7-FashionMNISTConvolutionsVisualizations.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "R6gHiH-I7uFa"
   },
   "source": [
    "# How does the model see?\n",
    "\n",
    "Lets retrain our convolutional model for the Fashion-MNIST dataset and then visualize the filters and pooling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C0tFgT1MMKi6",
    "outputId": "5850e18c-3f74-4413-b8ce-b061ff9e44df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_2 (Conv2D)            (None, 26, 26, 64)        640       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 11, 11, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1600)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 20)                32020     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                210       \n",
      "=================================================================\n",
      "Total params: 69,798\n",
      "Trainable params: 69,798\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5161 - accuracy: 0.8153 - val_loss: 0.3862 - val_accuracy: 0.8601\n",
      "Epoch 2/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3406 - accuracy: 0.8774 - val_loss: 0.3269 - val_accuracy: 0.8822\n",
      "Epoch 3/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2945 - accuracy: 0.8950 - val_loss: 0.3115 - val_accuracy: 0.8885\n",
      "Epoch 4/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2653 - accuracy: 0.9033 - val_loss: 0.3027 - val_accuracy: 0.8916\n",
      "Epoch 5/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2414 - accuracy: 0.9111 - val_loss: 0.2707 - val_accuracy: 0.9010\n",
      "Epoch 6/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2232 - accuracy: 0.9178 - val_loss: 0.2778 - val_accuracy: 0.8994\n",
      "Epoch 7/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2068 - accuracy: 0.9240 - val_loss: 0.2646 - val_accuracy: 0.9027\n",
      "Epoch 8/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1931 - accuracy: 0.9282 - val_loss: 0.2711 - val_accuracy: 0.9016\n",
      "Epoch 9/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1837 - accuracy: 0.9315 - val_loss: 0.2655 - val_accuracy: 0.9066\n",
      "Epoch 10/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1673 - accuracy: 0.9381 - val_loss: 0.2618 - val_accuracy: 0.9089\n",
      "Epoch 11/20\n",
      "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1575 - accuracy: 0.9424 - val_loss: 0.2808 - val_accuracy: 0.9017\n",
      "Epoch 12/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1491 - accuracy: 0.9439 - val_loss: 0.2711 - val_accuracy: 0.9112\n",
      "Epoch 13/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1385 - accuracy: 0.9484 - val_loss: 0.2902 - val_accuracy: 0.9060\n",
      "Epoch 14/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1320 - accuracy: 0.9517 - val_loss: 0.2748 - val_accuracy: 0.9069\n",
      "Epoch 15/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1245 - accuracy: 0.9531 - val_loss: 0.2988 - val_accuracy: 0.9075\n",
      "Epoch 16/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1172 - accuracy: 0.9565 - val_loss: 0.3046 - val_accuracy: 0.9069\n",
      "Epoch 17/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1108 - accuracy: 0.9583 - val_loss: 0.3028 - val_accuracy: 0.9113\n",
      "Epoch 18/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1043 - accuracy: 0.9612 - val_loss: 0.3380 - val_accuracy: 0.9104\n",
      "Epoch 19/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1026 - accuracy: 0.9617 - val_loss: 0.3316 - val_accuracy: 0.9072\n",
      "Epoch 20/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0933 - accuracy: 0.9657 - val_loss: 0.3219 - val_accuracy: 0.9109\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f7a10fc8748>"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "mnist = tf.keras.datasets.fashion_mnist\n",
    "(training_images, training_labels), (val_images, val_labels) = mnist.load_data()\n",
    "training_images=training_images.reshape(60000, 28, 28, 1)\n",
    "training_images=training_images / 255.0\n",
    "val_images=val_images.reshape(10000, 28, 28, 1)\n",
    "val_images=val_images/255.0\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Conv2D(64, (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.Flatten(),\n",
    "  tf.keras.layers.Dense(20, activation='relu'),\n",
    "  tf.keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()\n",
    "model.fit(training_images, training_labels, validation_data=(val_images, val_labels), epochs=20)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "IXx_LX3SAlFs"
   },
   "source": [
    "# Visualizing the Convolutions and Pooling\n",
    "\n",
    "This code will show us the convolutions graphically. The print (test_labels[:100]) shows us the first 100 labels in the test set, and you can see that the ones at index 0, index 23 and index 28 are all the same value (9). They're all shoes. Let's take a look at the result of running the convolution on each, and you'll begin to see common features between them emerge. Now, when the final dense layers are trained on this resulting data, it's working with a lot less, more targeted, data -- the features generated by this convolution/pooling combination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f-6nX4QsOku6",
    "outputId": "dd0b283b-1e69-41b1-a2f4-948c1841cbe6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9 2 1 1 6 1 4 6 5 7 4 5 7 3 4 1 2 4 8 0 2 5 7 9 1 4 6 0 9 3 8 8 3 3 8 0 7\n",
      " 5 7 9 6 1 3 7 6 7 2 1 2 2 4 4 5 8 2 2 8 4 8 0 7 7 8 5 1 1 2 3 9 8 7 0 2 6\n",
      " 2 3 1 2 8 4 1 8 5 9 5 0 3 2 0 6 5 3 6 7 1 8 0 1 4 2]\n"
     ]
    }
   ],
   "source": [
    "print(val_labels[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "9FGsHhv6JvDx",
    "outputId": "1b1417c0-802c-412b-83d9-e5d89a80b6e1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ku0gkFHaRSCjsIpFQ2EUiobCLRKKsSzan+iXuKy+E+tGlGHRmF4mEwi4SCYVdJBIKu0gkFHaRSCjsIpFQ2EUikRp2kgtJvkLyXZK7SX4tuf1xkkdIvpV8rSt9c0UkXzO5qGYMwMNm9ibJVgBvkHw5qX3XzJ4oXfNEpFhmsj57P4D+5OchknsALCh1w0SkuD7Te3aSiwGsBrAjuelBku+Q3EKyPbDNRpI9JHtGkSuosSKSvxmHnWQLgJ8CeMjMBgH8AMAyAKsweeb/9nTbmdlmM+s2s+5a1BehySKSjxmFnWQtJoP+YzN7FgDM7LiZjZvZBIAnAawpXTNFpFAz+TSeAH4IYI+ZfWfK7V1T7nYvgF3Fb56IFMtMPo2/BcCXAewk+dFast8EsIHkKgAGoBfAV0rSQhEpipl8Gv8qgOnmoX6x+M0RkVLRFXQikVDYRSKhsItEQmEXiYTCLhIJhV0kEgq7SCQUdpFIKOwikVDYRSKhsItEQmEXiYTCLhIJhV0kEjSz8u2MPAng0JSb5gL4sGwN+GyqtW3V2i5AbctXMdu2yMzmTVcoa9g/tXOyx8y6K9YAR7W2rVrbBaht+SpX2/QyXiQSCrtIJCod9s0V3r+nWttWre0C1LZ8laVtFX3PLiLlU+kzu4iUicIuEomKhJ3knSR/QXIfyUcq0YYQkr0kdybLUPdUuC1bSJ4guWvKbR0kXya5N/k+7Rp7FWpbVSzj7SwzXtFjV+nlz8v+np1kFsD7AH4LQB+A1wFsMLN3y9qQAJK9ALrNrOIXYJD8dQDnAPyTmV2f3PYtAANmtin5j7LdzL5RJW17HMC5Si/jnaxW1DV1mXEA9wD4I1Tw2Dntug9lOG6VOLOvAbDPzA6Y2QiApwGsr0A7qp6ZbQcwcMnN6wFsTX7eisknS9kF2lYVzKzfzN5Mfh4C8NEy4xU9dk67yqISYV8A4PCU3/tQXeu9G4CXSL5BcmOlGzONTjPrT34+BqCzko2ZRuoy3uV0yTLjVXPs8ln+vFD6gO7TbjWzGwHcBeCrycvVqmST78Gqqe90Rst4l8s0y4x/rJLHLt/lzwtVibAfAbBwyu9XJbdVBTM7knw/AeA5VN9S1Mc/WkE3+X6iwu35WDUt4z3dMuOogmNXyeXPKxH21wEsJ7mEZB2ALwJ4oQLt+BSSzckHJyDZDOAOVN9S1C8AuD/5+X4Az1ewLZ9QLct4h5YZR4WPXcWXPzezsn8BWIfJT+T3A/iLSrQh0K6lAN5OvnZXum0AnsLky7pRTH628QCAOQC2AdgL4D8BdFRR2/4ZwE4A72AyWF0VatutmHyJ/g6At5KvdZU+dk67ynLcdLmsSCT0AZ1IJBR2kUgo7CKRUNhFIqGwi0RCYReJhMIuEon/B35IdcWYLEDsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def show_image(img):\n",
    "  plt.figure()\n",
    "  plt.imshow(val_images[img].reshape(28,28))\n",
    "  plt.grid(False)\n",
    "  plt.show()  \n",
    "\n",
    "f, axarr = plt.subplots(3,2)\n",
    "# By scanning the list above I saw that the 0, 23 and 28 entries are all label 9 \n",
    "FIRST_IMAGE=0\n",
    "SECOND_IMAGE=23\n",
    "THIRD_IMAGE=28\n",
    "\n",
    "# For shoes (0, 23, 28), Convolution_Number=1 (i.e. the second filter) shows\n",
    "# the sole being filtered out very clearly\n",
    "\n",
    "CONVOLUTION_NUMBER = 1\n",
    "from tensorflow.keras import models\n",
    "layer_outputs = [layer.output for layer in model.layers]\n",
    "activation_model = tf.keras.models.Model(inputs = model.input, outputs = layer_outputs)\n",
    "for x in range(0,2):\n",
    "  f1 = activation_model.predict(val_images[FIRST_IMAGE].reshape(1, 28, 28, 1))[x]\n",
    "  axarr[0,x].imshow(f1[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')\n",
    "  axarr[0,x].grid(False)\n",
    "  f2 = activation_model.predict(val_images[SECOND_IMAGE].reshape(1, 28, 28, 1))[x]\n",
    "  axarr[1,x].imshow(f2[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')\n",
    "  axarr[1,x].grid(False)\n",
    "  f3 = activation_model.predict(val_images[THIRD_IMAGE].reshape(1, 28, 28, 1))[x]\n",
    "  axarr[2,x].imshow(f3[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')\n",
    "  axarr[2,x].grid(False)\n",
    "\n",
    "\n",
    "show_image(FIRST_IMAGE)\n",
    "show_image(SECOND_IMAGE)\n",
    "show_image(THIRD_IMAGE)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Fashion-MNIST-Convolutions.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}