<reponame>A-LittleSeven/distributedTensorFlow
import tensorflow as tf


def LeNet(imgs, n_classes, mode):
    """Model function for CNN."""
    # Input Layer
    # Reshape X to 4-D tensor: [batch_size, width, height, channels]
    # MNIST images are 28x28 pixels, and have one color channel
    input_layer = tf.reshape(imgs, [-1, 28, 28, 1], name="input_layer")

    tf.compat.v1.summary.image("input_layer", input_layer)

    # Convolutional Layer #1
    # Computes 32 features using a 5x5 filter with ReLU activation.
    # Padding is added to preserve width and height.
    # Input Tensor Shape: [batch_size, 28, 28, 1]
    # Output Tensor Shape: [batch_size, 28, 28, 32]
    conv1 = tf.layers.conv2d(
        inputs=input_layer,
        filters=32,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu,
        name="conv_1"
    )

    # Pooling Layer #1
    # First max pooling layer with a 2x2 filter and stride of 2
    # Input Tensor Shape: [batch_size, 28, 28, 32]
    # Output Tensor Shape: [batch_size, 14, 14, 32]
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

    # Convolutional Layer #2
    # Computes 64 features using a 5x5 filter.
    # Padding is added to preserve width and height.
    # Input Tensor Shape: [batch_size, 14, 14, 32]
    # Output Tensor Shape: [batch_size, 14, 14, 64]
    conv2 = tf.layers.conv2d(
        inputs=pool1,
        filters=64,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu,
        name="conv_2"
    )

    # Pooling Layer #2
    # Second max pooling layer with a 2x2 filter and stride of 2
    # Input Tensor Shape: [batch_size, 14, 14, 64]
    # Output Tensor Shape: [batch_size, 7, 7, 64]
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

    # Flatten tensor into a batch of vectors
    # Input Tensor Shape: [batch_size, 7, 7, 64]
    # Output Tensor Shape: [batch_size, 7 * 7 * 64]
    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])

    # Dense Layer
    # Densely connected layer with 1024 neurons
    # Input Tensor Shape: [batch_size, 7 * 7 * 64]
    # Output Tensor Shape: [batch_size, 1024]
    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu, name="fc_1")

    # Add dropout operation; 0.6 probability that element will be kept
    dropout = tf.layers.dropout(inputs=dense, rate=0.4, training=mode)

    # Logits layer
    # Input Tensor Shape: [batch_size, 1024]
    # Output Tensor Shape: [batch_size, 10]
    logits = tf.layers.dense(inputs=dropout, units=n_classes, name="logits")
    logits = tf.identity(logits, 'y_')
    return logits


def losses(logits, labels):
    """
    Compute loss from logits and labels
    Args:
        logits: logits tensor, float, [batch_size, n_classes]
        labels: label tensor, tf.int32, [batch_size]
    Returns:
        loss tensor of float type
    """
    with tf.compat.v1.variable_scope('loss') as scope:
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits \
            (logits=logits, labels=labels, name='xentropy_per_example')
        loss = tf.reduce_mean(cross_entropy, name='loss')
        tf.compat.v1.summary.scalar(scope.name + '/loss', loss)
    return loss


def training(loss, learning_rate, global_step):
    """
    Training ops, the Op returned by this function is what must be passed to
        'sess.run()' call to cause the model to train.
    Args:
        loss: loss tensor, from losses()
    Returns:
        train_op: The op for trainning
    """
    with tf.name_scope('optimizer'):
        train_op = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss, global_step=global_step)
    return train_op


def evaluation(logits, labels):
    """
    Evaluate the quality of the logits at predicting the label.
    Args:
        logits: Logits tensor, float - [batch_size, NUM_CLASSES].
        labels: Labels tensor, int32 - [batch_size], with values in the
        range [0, NUM_CLASSES).
    Returns:
        A scalar int32 tensor with the number of examples (out of batch_size)
        that were predicted correctly.
    """
    with tf.compat.v1.variable_scope('accuracy') as scope:
        correct = tf.nn.in_top_k(logits, labels, 1)
        correct = tf.cast(correct, tf.float16)
        accuracy = tf.reduce_mean(correct)
        tf.compat.v1.summary.scalar(scope.name + '/accuracy', accuracy)
    return accuracy

import tensorflow as tf


def LeNet(imgs, n_classes, mode):
    """Model function for CNN."""
    # Input Layer
    # Reshape X to 4-D tensor: [batch_size, width, height, channels]
    # MNIST images are 28x28 pixels, and have one color channel
    input_layer = tf.reshape(imgs, [-1, 28, 28, 1], name="input_layer")

    tf.compat.v1.summary.image("input_layer", input_layer)

    # Convolutional Layer #1
    # Computes 32 features using a 5x5 filter with ReLU activation.
    # Padding is added to preserve width and height.
    # Input Tensor Shape: [batch_size, 28, 28, 1]
    # Output Tensor Shape: [batch_size, 28, 28, 32]
    conv1 = tf.layers.conv2d(
        inputs=input_layer,
        filters=32,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu,
        name="conv_1"
    )

    # Pooling Layer #1
    # First max pooling layer with a 2x2 filter and stride of 2
    # Input Tensor Shape: [batch_size, 28, 28, 32]
    # Output Tensor Shape: [batch_size, 14, 14, 32]
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

    # Convolutional Layer #2
    # Computes 64 features using a 5x5 filter.
    # Padding is added to preserve width and height.
    # Input Tensor Shape: [batch_size, 14, 14, 32]
    # Output Tensor Shape: [batch_size, 14, 14, 64]
    conv2 = tf.layers.conv2d(
        inputs=pool1,
        filters=64,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu,
        name="conv_2"
    )

    # Pooling Layer #2
    # Second max pooling layer with a 2x2 filter and stride of 2
    # Input Tensor Shape: [batch_size, 14, 14, 64]
    # Output Tensor Shape: [batch_size, 7, 7, 64]
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

    # Flatten tensor into a batch of vectors
    # Input Tensor Shape: [batch_size, 7, 7, 64]
    # Output Tensor Shape: [batch_size, 7 * 7 * 64]
    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])

    # Dense Layer
    # Densely connected layer with 1024 neurons
    # Input Tensor Shape: [batch_size, 7 * 7 * 64]
    # Output Tensor Shape: [batch_size, 1024]
    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu, name="fc_1")

    # Add dropout operation; 0.6 probability that element will be kept
    dropout = tf.layers.dropout(inputs=dense, rate=0.4, training=mode)

    # Logits layer
    # Input Tensor Shape: [batch_size, 1024]
    # Output Tensor Shape: [batch_size, 10]
    logits = tf.layers.dense(inputs=dropout, units=n_classes, name="logits")
    logits = tf.identity(logits, 'y_')
    return logits


def losses(logits, labels):
    """
    Compute loss from logits and labels
    Args:
        logits: logits tensor, float, [batch_size, n_classes]
        labels: label tensor, tf.int32, [batch_size]
    Returns:
        loss tensor of float type
    """
    with tf.compat.v1.variable_scope('loss') as scope:
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits \
            (logits=logits, labels=labels, name='xentropy_per_example')
        loss = tf.reduce_mean(cross_entropy, name='loss')
        tf.compat.v1.summary.scalar(scope.name + '/loss', loss)
    return loss


def training(loss, learning_rate, global_step):
    """
    Training ops, the Op returned by this function is what must be passed to
        'sess.run()' call to cause the model to train.
    Args:
        loss: loss tensor, from losses()
    Returns:
        train_op: The op for trainning
    """
    with tf.name_scope('optimizer'):
        train_op = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss, global_step=global_step)
    return train_op


def evaluation(logits, labels):
    """
    Evaluate the quality of the logits at predicting the label.
    Args:
        logits: Logits tensor, float - [batch_size, NUM_CLASSES].
        labels: Labels tensor, int32 - [batch_size], with values in the
        range [0, NUM_CLASSES).
    Returns:
        A scalar int32 tensor with the number of examples (out of batch_size)
        that were predicted correctly.
    """
    with tf.compat.v1.variable_scope('accuracy') as scope:
        correct = tf.nn.in_top_k(logits, labels, 1)
        correct = tf.cast(correct, tf.float16)
        accuracy = tf.reduce_mean(correct)
        tf.compat.v1.summary.scalar(scope.name + '/accuracy', accuracy)
    return accuracy
Model function for CNN. # Input Layer # Reshape X to 4-D tensor: [batch_size, width, height, channels] # MNIST images are 28x28 pixels, and have one color channel # Convolutional Layer #1 # Computes 32 features using a 5x5 filter with ReLU activation. # Padding is added to preserve width and height. # Input Tensor Shape: [batch_size, 28, 28, 1] # Output Tensor Shape: [batch_size, 28, 28, 32] # Pooling Layer #1 # First max pooling layer with a 2x2 filter and stride of 2 # Input Tensor Shape: [batch_size, 28, 28, 32] # Output Tensor Shape: [batch_size, 14, 14, 32] # Convolutional Layer #2 # Computes 64 features using a 5x5 filter. # Padding is added to preserve width and height. # Input Tensor Shape: [batch_size, 14, 14, 32] # Output Tensor Shape: [batch_size, 14, 14, 64] # Pooling Layer #2 # Second max pooling layer with a 2x2 filter and stride of 2 # Input Tensor Shape: [batch_size, 14, 14, 64] # Output Tensor Shape: [batch_size, 7, 7, 64] # Flatten tensor into a batch of vectors # Input Tensor Shape: [batch_size, 7, 7, 64] # Output Tensor Shape: [batch_size, 7 * 7 * 64] # Dense Layer # Densely connected layer with 1024 neurons # Input Tensor Shape: [batch_size, 7 * 7 * 64] # Output Tensor Shape: [batch_size, 1024] # Add dropout operation; 0.6 probability that element will be kept # Logits layer # Input Tensor Shape: [batch_size, 1024] # Output Tensor Shape: [batch_size, 10] Compute loss from logits and labels     Args:         logits: logits tensor, float, [batch_size, n_classes]         labels: label tensor, tf.int32, [batch_size]     Returns:         loss tensor of float type Training ops, the Op returned by this function is what must be passed to         'sess.run()' call to cause the model to train.     Args:         loss: loss tensor, from losses()     Returns:         train_op: The op for trainning Evaluate the quality of the logits at predicting the label.     Args:         logits: Logits tensor, float - [batch_size, NUM_CLASSES].         labels: Labels tensor, int32 - [batch_size], with values in the         range [0, NUM_CLASSES).     Returns:         A scalar int32 tensor with the number of examples (out of batch_size)         that were predicted correctly.