text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import tensorflow as tf
  from sklearn.utils import shuffle
  
  tf.logging.set_verbosity(tf.logging.INFO)
  
  from tensorflow.examples.tutorials.mnist import input_data
  
  DATA_DIR = './data/fashion'
  
  
  def cnn_model_fn(features, labels, 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(features["x"], [-1, 28, 28, 1])
  
      # 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",
          kernel_initializer='glorot_uniform',
          activation=tf.nn.leaky_relu)
  
      # 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",
          kernel_initializer='glorot_uniform',
          activation=tf.nn.leaky_relu)
  
      # 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.leaky_relu)
  
      # Add dropout operation; 0.6 probability that element will be kept
      # dropout = tf.layers.dropout(
      #     inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
  
      # Logits layer
      # Input Tensor Shape: [batch_size, 1024]
      # Output Tensor Shape: [batch_size, 10]
      logits = tf.layers.dense(inputs=dense, units=10)
  
      predictions = {
          # Generate predictions (for PREDICT and EVAL mode)
          "classes": tf.argmax(input=logits, axis=1),
          # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
          # `logging_hook`.
          "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
      }
      if mode == tf.estimator.ModeKeys.PREDICT:
          return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
  
      # Calculate Loss (for both TRAIN and EVAL modes)
      onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
      loss = tf.losses.softmax_cross_entropy(
          onehot_labels=onehot_labels, logits=logits)
  
      # Configure the Training Op (for TRAIN mode)
      if mode == tf.estimator.ModeKeys.TRAIN:
          optimizer = tf.train.AdagradOptimizer(learning_rate=0.01)
          train_op = optimizer.minimize(
              loss=loss,
              global_step=tf.train.get_global_step())
          return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
  
      # Add evaluation metrics (for EVAL mode)
      eval_metric_ops = {
          "accuracy": tf.metrics.accuracy(
              labels=labels, predictions=predictions["classes"])}
      return tf.estimator.EstimatorSpec(
          mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
  
  
  def main(unused_argv):
      # Load training and eval data
      mnist = input_data.read_data_sets(DATA_DIR, one_hot=False, validation_size=0)
      train_data = mnist.train.images  # Returns np.array
      train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
      train_data, train_labels = shuffle(train_data, train_labels)
      eval_data = mnist.test.images  # Returns np.array
      eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
      eval_data, eval_labels = shuffle(eval_data, eval_labels)
  
      # Create the Estimator
      mnist_classifier = tf.estimator.Estimator(
          model_fn=cnn_model_fn, model_dir="/tmp/mnist_adagrad_optimizer_model")
  
      # Train the model
      train_input_fn = tf.estimator.inputs.numpy_input_fn(
          x={"x": train_data},
          y=train_labels,
          batch_size=200,
          num_epochs=None,
          shuffle=True)
  
      # Evaluate the model and print results
      eval_input_fn = tf.estimator.inputs.numpy_input_fn(
          x={"x": eval_data},
          y=eval_labels,
          num_epochs=1,
          shuffle=False)
  
      for j in range(10):
          mnist_classifier.train(
              input_fn=train_input_fn,
              steps=2000)
  
          eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
          print(eval_results)
  
  
  if __name__ == "__main__":
      tf.app.run()
  