MLP/mnist_tensorflow.ipynb

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   "source": [
    "import tensorflow as tf\n",
    "print(\"TensorFlow version:\", tf.__version__)\n",
    "\n",
    "from tensorflow.keras.layers import Dense, Flatten, Conv2D\n",
    "from tensorflow.keras import Model\n",
    "\n",
    "\n",
    "mnist = tf.keras.datasets.mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data(path='/root/projects/yesu/deep_learning/Neural_Network/data_sets/mnist.pkl.gz' )\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "\n",
    "# Add a channels dimension\n",
    "x_train = x_train[..., tf.newaxis].astype(\"float32\")\n",
    "x_test = x_test[..., tf.newaxis].astype(\"float32\")\n",
    "# 使用 `tf.data` 来将数据集切分为 batch 以及混淆数据集：\n",
    "train_ds = tf.data.Dataset.from_tensor_slices(\n",
    "    (x_train, y_train)).shuffle(10000).batch(32)\n",
    "\n",
    "test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)\n",
    "# 使用 Keras  构建 `tf.keras` 模型：\n",
    "class MyModel(Model):\n",
    "  def __init__(self):\n",
    "    super(MyModel, self).__init__()\n",
    "    self.conv1 = Conv2D(32, 3, activation='relu')\n",
    "    self.flatten = Flatten()\n",
    "    self.d1 = Dense(128, activation='relu')\n",
    "    self.d2 = Dense(10)\n",
    "\n",
    "  def call(self, x):\n",
    "    x = self.conv1(x)\n",
    "    x = self.flatten(x)\n",
    "    x = self.d1(x)\n",
    "    return self.d2(x)\n",
    "\n",
    "# Create an instance of the model\n",
    "model = MyModel()\n",
    "# 为训练选择优化器与损失函数： \n",
    "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
    "\n",
    "optimizer = tf.keras.optimizers.Adam()\n",
    "# 选择衡量指标来度量模型的损失值（loss）和准确率（accuracy）。这些指标在 epoch 上累积值，然后打印出整体结果。\n",
    "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
    "train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n",
    "\n",
    "test_loss = tf.keras.metrics.Mean(name='test_loss')\n",
    "test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')\n",
    "# 使用 `tf.GradientTape` 来训练模型：\n",
    "@tf.function\n",
    "def train_step(images, labels):\n",
    "  with tf.GradientTape() as tape:\n",
    "    # training=True is only needed if there are layers with different\n",
    "    # behavior during training versus inference (e.g. Dropout).\n",
    "    predictions = model(images, training=True)\n",
    "    loss = loss_object(labels, predictions)\n",
    "  gradients = tape.gradient(loss, model.trainable_variables)\n",
    "  optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "\n",
    "  train_loss(loss)\n",
    "  train_accuracy(labels, predictions)\n",
    "# 测试模型：\n",
    "@tf.function\n",
    "def test_step(images, labels):\n",
    "  # training=False is only needed if there are layers with different\n",
    "  # behavior during training versus inference (e.g. Dropout).\n",
    "  predictions = model(images, training=False)\n",
    "  t_loss = loss_object(labels, predictions)\n",
    "\n",
    "  test_loss(t_loss)\n",
    "  test_accuracy(labels, predictions)\n",
    "EPOCHS = 5\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "  # Reset the metrics at the start of the next epoch\n",
    "  train_loss.reset_state()\n",
    "  train_accuracy.reset_state()\n",
    "  test_loss.reset_state()\n",
    "  test_accuracy.reset_state()\n",
    "\n",
    "  for images, labels in train_ds:\n",
    "    train_step(images, labels)\n",
    "\n",
    "  for test_images, test_labels in test_ds:\n",
    "    test_step(test_images, test_labels)\n",
    "\n",
    "  print(\n",
    "    f'Epoch {epoch + 1}, '\n",
    "    f'Loss: {train_loss.result()}, '\n",
    "    f'Accuracy: {train_accuracy.result() * 100}, '\n",
    "    f'Test Loss: {test_loss.result()}, '\n",
    "    f'Test Accuracy: {test_accuracy.result() * 100}'\n",
    "  )"
   ]
  }
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