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- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- import numpy as np
- import tensorflow as tf
- 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=[5, 5],
- 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=[5, 5],
- 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=0.4, 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)
- 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)
- 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)
- 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)
- eval_data = mnist.test.images
- eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
- mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_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=100,
- 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": 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()
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