tf_test.py

import tensorflow as tf
import numpy as np

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


def cnn_model_fn(features, labels, mode):
    input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])

    conv1 = tf.layers.conv2d(inputs=input_layer,
                             filters=32,
                             kernel_size=[3, 3],
                             padding="same",
                             activation=tf.nn.relu)

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

    conv2 = tf.layers.conv2d(inputs=pool1,
                             filters=64,
                             kernel_size=[3, 3],
                             padding="same",
                             activation=tf.nn.relu)

    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
    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=0.5,
                                training=(mode == tf.estimator.ModeKeys.TRAIN)
                               )

    logits = tf.layers.dense(inputs=dropout, units=10)
    predictions = {
                   "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)

    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
        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)

    assert mode == tf.estimator.ModeKeys.EVAL
    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):
    mnist = tf.contrib.learn.datasets.load_dataset("mnist")
    train_data = mnist.train.images
    train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
    test_data = mnist.test.images
    test_labels = np.asarray(mnist.test.labels, dtype=np.int32)
    mnist_classifier = tf.estimator.Estimator(
        model_fn=cnn_model_fn, model_dir="./tf_mnist_model/")
    tensors_to_log = {"probabilities": "softmax_tensor"}
    logging_hook = tf.train.LoggingTensorHook(
        tensors=tensors_to_log, every_n_iter=50)

    train_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"x": train_data},
        y=train_labels,
        batch_size=128,
        num_epochs=None,
        shuffle=True,
    )
    mnist_classifier.train(
        input_fn=train_input_fn,
        steps=20000,
        hooks=[logging_hook])

    eval_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"x": test_data},
        y=test_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()