tf_fashion_mnist_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "515aa66e-7b9f-4c6d-8198-751400ef2f3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout, MaxPooling2D\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# list of all physical devices\n",
    "print(tf.config.list_physical_devices())\n",
    "\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "tf.config.set_visible_devices(gpus[0], 'GPU')\n",
    "\n",
    "# To exclusively select CPU - uncomment below and comment GPU section above\n",
    "# tf.config.set_visible_devices([], 'GPU')\n",
    "\n",
    "# tf.debugging.set_log_device_placement(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab370a3b-d311-41e7-abb8-1f6fb291655c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1 Max\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-15 13:47:58.982191: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-05-15 13:47:58.982298: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 64)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 14, 14, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 14, 14, 64)        0         \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 14, 14, 32)        8224      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 7, 7, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 7, 7, 32)          0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1568)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               401664    \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                2570      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 412,778\n",
      "Trainable params: 412,778\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "# Must define the input shape in the first layer of the neural network\n",
    "model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', input_shape=(28,28,1))) \n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "# Take a look at the model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "682ef519-6708-452f-a538-c9ed257697b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-15 13:47:59.991293: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2024-05-15 13:48:00.326802: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "938/938 [==============================] - 19s 17ms/step - loss: 0.5532 - accuracy: 0.7994\n",
      "Epoch 2/10\n",
      "938/938 [==============================] - 16s 17ms/step - loss: 0.3777 - accuracy: 0.8647\n",
      "Epoch 3/10\n",
      "938/938 [==============================] - 16s 17ms/step - loss: 0.3329 - accuracy: 0.8805\n",
      "Epoch 4/10\n",
      "938/938 [==============================] - 16s 17ms/step - loss: 0.3067 - accuracy: 0.8906\n",
      "Epoch 5/10\n",
      "938/938 [==============================] - 16s 17ms/step - loss: 0.2926 - accuracy: 0.8936\n",
      "Epoch 6/10\n",
      "938/938 [==============================] - 16s 17ms/step - loss: 0.2857 - accuracy: 0.8971\n",
      "Epoch 7/10\n",
      "938/938 [==============================] - 16s 17ms/step - loss: 0.2783 - accuracy: 0.8996\n",
      "Epoch 8/10\n",
      "938/938 [==============================] - 16s 17ms/step - loss: 0.2750 - accuracy: 0.9018\n",
      "Epoch 9/10\n",
      "938/938 [==============================] - 16s 17ms/step - loss: 0.2705 - accuracy: 0.9042\n",
      "Epoch 10/10\n",
      "938/938 [==============================] - 16s 17ms/step - loss: 0.2653 - accuracy: 0.9062\n",
      " 13/313 [>.............................] - ETA: 2s - loss: 0.2488 - accuracy: 0.9183"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-15 13:50:42.609108: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 3s 9ms/step - loss: 0.2755 - accuracy: 0.9026\n",
      "Fitting took: 165.490905046463\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9025999903678894"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "import time\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
    "train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "train_images = train_images.astype('float32') / 255\n",
    "test_images = test_images.reshape((10000, 28, 28, 1))\n",
    "test_images = test_images.astype('float32') / 255\n",
    "train_labels = to_categorical(train_labels)\n",
    "test_labels = to_categorical(test_labels)\n",
    "start = time.time()\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(train_images, train_labels, epochs=10, batch_size=64)\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "print(\"Fitting took: {}\".format(time.time() - start))\n",
    "test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cbcf422-412c-4e45-acd0-8ecfdb45a794",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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