CNN Deep Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import models\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "29515/29515 [==============================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26421880/26421880 [==============================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "5148/5148 [==============================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4422102/4422102 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "fmnist = tf.keras.datasets.fashion_mnist\n",
    "(training_images, training_labels), (test_images, test_labels) = fmnist.load_data()\n",
    "\n",
    "# Normalize the pixel values\n",
    "training_images = training_images / 255.0\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-29 09:35:16.127397: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-06-29 09:35:16.127441: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-06-29 09:35:16.127464: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (codespaces-962027): /proc/driver/nvidia/version does not exist\n",
      "2022-06-29 09:35:16.127986: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MODEL TRAINING:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-29 09:35:16.502170: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 188160000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4979 - accuracy: 0.8261\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3742 - accuracy: 0.8661\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3364 - accuracy: 0.8778\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3108 - accuracy: 0.8854\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2937 - accuracy: 0.8926\n",
      "\n",
      "MODEL EVALUATION:\n",
      "  1/313 [..............................] - ETA: 47s - loss: 0.2520 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-29 09:35:34.402392: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 31360000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 1ms/step - loss: 0.3514 - accuracy: 0.8760\n"
     ]
    }
   ],
   "source": [
    "# Normal Shallow Network\n",
    "# Define the model\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Flatten(),\n",
    "  tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
    "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])\n",
    "\n",
    "# Setup training parameters\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "print(f'\\nMODEL TRAINING:')\n",
    "model.fit(training_images, training_labels, epochs=5)\n",
    "\n",
    "# Evaluate on the test set\n",
    "print(f'\\nMODEL EVALUATION:')\n",
    "test_loss = model.evaluate(test_images, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_2 (Conv2D)           (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 13, 13, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 11, 11, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 5, 5, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 800)               0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 128)               102528    \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 113,386\n",
      "Trainable params: 113,386\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "MODEL TRAINING:\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 26s 14ms/step - loss: 0.4758 - accuracy: 0.8267\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 24s 13ms/step - loss: 0.3222 - accuracy: 0.8824\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 24s 13ms/step - loss: 0.2745 - accuracy: 0.8994\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 24s 13ms/step - loss: 0.2457 - accuracy: 0.9083\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 24s 13ms/step - loss: 0.2227 - accuracy: 0.9175\n",
      "\n",
      "MODEL EVALUATION:\n",
      "313/313 [==============================] - 1s 4ms/step - loss: 0.2774 - accuracy: 0.8975\n"
     ]
    }
   ],
   "source": [
    "# Define the model\n",
    "model = tf.keras.models.Sequential([\n",
    "                                                         \n",
    "  # Add convolutions and max pooling\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(32, (3,3), activation='relu'),\n",
    "  tf.keras.layers.MaxPooling2D(2,2),\n",
    "\n",
    "  # Add the same layers as before\n",
    "  tf.keras.layers.Flatten(),\n",
    "  tf.keras.layers.Dense(128, activation='relu'),\n",
    "  tf.keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "# Print the model summary\n",
    "model.summary()\n",
    "\n",
    "# Use same settings\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "print(f'\\nMODEL TRAINING:')\n",
    "model.fit(training_images, training_labels, epochs=5)\n",
    "\n",
    "# Evaluate on the test set\n",
    "print(f'\\nMODEL EVALUATION:')\n",
    "test_loss = model.evaluate(test_images, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       7, 9, 1, 4, 6, 0, 9, 3, 8, 8, 3, 3, 8, 0, 7, 5, 7, 9, 6, 1, 3, 7,\n",
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      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 65ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(3,4)\n",
    "\n",
    "FIRST_IMAGE=0\n",
    "SECOND_IMAGE=23\n",
    "THIRD_IMAGE=28\n",
    "CONVOLUTION_NUMBER = 1\n",
    "\n",
    "layer_outputs = [layer.output for layer in model.layers]\n",
    "activation_model = tf.keras.models.Model(inputs = model.input, outputs = layer_outputs)\n",
    "\n",
    "for x in range(0,4):\n",
    "  f1 = activation_model.predict(test_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",
    "  \n",
    "  f2 = activation_model.predict(test_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",
    "  \n",
    "  f3 = activation_model.predict(test_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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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