cnn_mnist.py

import numpy as np
import tensorflow as tf

tf.logging.set_verbosity(tf.logging.INFO)


def cnn_model_fn(features, labels, mode):
    """Model function for CNN"""
    # Input Layer
    with tf.name_scope("Input_Layer"):
        input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])

    # Convolutional Layer #1
    conv1 = tf.layers.conv2d(
        inputs=input_layer,
        filters=32,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu
        )

    # Pooling Layer #1
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

    # Convolutional Layer # 2 and Pooling Layer #2

    conv2 = tf.layers.conv2d(
        inputs=pool1,
        filters=64,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu
    )

    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

    # Dense Layer

    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
    dropout = tf.layers.dropout(
        inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN
    )

    # Logits Layer
    logits = tf.layers.dense(inputs=dropout, units=10)

    predictions = {
        # Generate predictions (for PREDICT and EVAL Mode)
        "classes": tf.argmax(input=logits, axis=1),
        "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
    )
    learning_rate = tf.train.exponential_decay(0.001, tf.train.get_global_step(), 100, 0.96, staircase=True)
    # Configure the Training Op (for TRAIN mode)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
        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_metrics_ops = {
        "accuracy": tf.metrics.accuracy(
            labels=labels, predictions=predictions["classes"]
        )
    }

    return tf.estimator.EstimatorSpec(
        mode=mode, loss=loss, eval_metric_ops=eval_metrics_ops
    )


def main(unused_argv):
    # Load training data and eval
    mnist = tf.contrib.learn.datasets.load_dataset("mnist")
    train_data = mnist.train.images
    train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
    eval_data = mnist.test.images
    eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

    # Create the Estimator
    mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")

    # Set up logging for predictions
    tensors_to_log = {"probabilities": "softmax_tensor"}
    logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)

    # Train the model
    train_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"x": train_data},
        y=train_labels,
        batch_size=100,
        num_epochs=1,
        shuffle=True
    )

    mnist_classifier.train(
        input_fn=train_input_fn,
        hooks=[logging_hook]
    )
    # 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
    )
    eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
    print(eval_results)


if __name__ == "__main__":
    tf.app.run()