cnn_tf_graph_mnist.py

import tensorflow as tf
from tensorflow.contrib import learn


def cnn_model(features, mode):
	"""Model function for CNN."""
	# Input Layer
	#input_layer = tf.reshape(features, [-1, 28, 28, 1])
	input_layer = features

	# 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, 1048576])
	dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
	dropout = tf.layers.dropout(
			inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)

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

	return logits

#
	#loss = None
	#train_op = None
#
	## Calculate Loss (for both TRAIN and EVAL modes)
	#if mode != learn.ModeKeys.INFER:
		#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 == learn.ModeKeys.TRAIN:
		#train_op = tf.contrib.layers.optimize_loss(
				#loss=loss,
				#global_step=tf.contrib.framework.get_global_step(),
				#learning_rate=0.001,
				#optimizer="SGD")
#
	## Generate Predictions
	#predictions = {
			#"classes": tf.argmax(
					#input=logits, axis=1),
			#"probabilities": tf.nn.softmax(
					#logits, name="softmax_tensor")
	#}
#
	## Return a ModelFnOps object
	#return model_fn_lib.ModelFnOps(
			#mode=mode, predictions=predictions, loss=loss, train_op=train_op)