import tensorflow as tf

from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout
from tensorflow.keras import Model

train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

EPOCHS = 5

class MyModel(Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.conv1 = Conv2D(32, (3, 3), activation='relu')
    self.conv2 = Conv2D(64, (3, 3), activation='relu')
    self.pool1 = MaxPool2D((2, 2))
    self.pool2 = MaxPool2D((2, 2))
    self.dropout1 = Dropout(0.25)
    self.dropout2 = Dropout(0.5)
    self.flatten = Flatten()
    self.d1 = Dense(128, activation='relu')
    self.d2 = Dense(10)

  def call(self, x):
    x = self.conv1(x)
    x = self.pool1(x)
    x = self.conv2(x)
    x = self.pool2(x)
    x = self.dropout1(x)
    x = self.flatten(x)
    x = self.d1(x)
    x = self.dropout2(x)
    x = self.d2(x)
    x = tf.nn.log_softmax(x)
    return x

def torch_test():
  pass

@tf.function
def train_step(images, labels):
  with tf.GradientTape() as tape:
    # training=True is only needed if there are layers with different
    # behavior during training versus inference (e.g. Dropout).
    predictions = model(images, training=True)
    loss = loss_object(labels, predictions)
  gradients = tape.gradient(loss, model.trainable_variables)
  optimizer.apply_gradients(zip(gradients, model.trainable_variables))

  train_loss(loss)
  train_accuracy(labels, predictions)

@tf.function
def test_step(images, labels):
  # training=False is only needed if there are layers with different
  # behavior during training versus inference (e.g. Dropout).
  predictions = model(images, training=False)
  t_loss = loss_object(labels, predictions)

  test_loss(t_loss)
  test_accuracy(labels, predictions)


if __name__ == '__main__':
  mnist = tf.keras.datasets.mnist

  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  x_train, x_test = x_train / 255.0, x_test / 255.0

  # Add a channels dimension
  x_train = x_train[..., tf.newaxis]
  x_test = x_test[..., tf.newaxis]

  train_ds = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train)).shuffle(10000).batch(32)

  test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

  # Create an instance of the model
  model = MyModel()

  loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

  optimizer = tf.keras.optimizers.Adam()


  for epoch in range(EPOCHS):
    # Reset the metrics at the start of the next epoch
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()

    for images, labels in train_ds:
      train_step(images, labels)

    for test_images, test_labels in test_ds:
      test_step(test_images, test_labels)

    template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
    print(template.format(epoch+1,
                          train_loss.result(),
                          train_accuracy.result()*100,
                          test_loss.result(),
                          test_accuracy.result()*100))

import tensorflow as tf

from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout
from tensorflow.keras import Model

train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

EPOCHS = 5

class MyModel(Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.conv1 = Conv2D(32, (3, 3), activation='relu')
    self.conv2 = Conv2D(64, (3, 3), activation='relu')
    self.pool1 = MaxPool2D((2, 2))
    self.pool2 = MaxPool2D((2, 2))
    self.dropout1 = Dropout(0.25)
    self.dropout2 = Dropout(0.5)
    self.flatten = Flatten()
    self.d1 = Dense(128, activation='relu')
    self.d2 = Dense(10)

  def call(self, x):
    x = self.conv1(x)
    x = self.pool1(x)
    x = self.conv2(x)
    x = self.pool2(x)
    x = self.dropout1(x)
    x = self.flatten(x)
    x = self.d1(x)
    x = self.dropout2(x)
    x = self.d2(x)
    x = tf.nn.log_softmax(x)
    return x

def torch_test():
  pass

@tf.function
def train_step(images, labels):
  with tf.GradientTape() as tape:
    # training=True is only needed if there are layers with different
    # behavior during training versus inference (e.g. Dropout).
    predictions = model(images, training=True)
    loss = loss_object(labels, predictions)
  gradients = tape.gradient(loss, model.trainable_variables)
  optimizer.apply_gradients(zip(gradients, model.trainable_variables))

  train_loss(loss)
  train_accuracy(labels, predictions)

@tf.function
def test_step(images, labels):
  # training=False is only needed if there are layers with different
  # behavior during training versus inference (e.g. Dropout).
  predictions = model(images, training=False)
  t_loss = loss_object(labels, predictions)

  test_loss(t_loss)
  test_accuracy(labels, predictions)


if __name__ == '__main__':
  mnist = tf.keras.datasets.mnist

  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  x_train, x_test = x_train / 255.0, x_test / 255.0

  # Add a channels dimension
  x_train = x_train[..., tf.newaxis]
  x_test = x_test[..., tf.newaxis]

  train_ds = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train)).shuffle(10000).batch(32)

  test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

  # Create an instance of the model
  model = MyModel()

  loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

  optimizer = tf.keras.optimizers.Adam()


  for epoch in range(EPOCHS):
    # Reset the metrics at the start of the next epoch
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()

    for images, labels in train_ds:
      train_step(images, labels)

    for test_images, test_labels in test_ds:
      test_step(test_images, test_labels)

    template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
    print(template.format(epoch+1,
                          train_loss.result(),
                          train_accuracy.result()*100,
                          test_loss.result(),
                          test_accuracy.result()*100))

# training=True is only needed if there are layers with different # behavior during training versus inference (e.g. Dropout). # training=False is only needed if there are layers with different # behavior during training versus inference (e.g. Dropout). # Add a channels dimension # Create an instance of the model # Reset the metrics at the start of the next epoch