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# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
# Common imports
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from datetime import datetime
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
root_logdir = "tf_logs"
logdir = "{}/run-{}/".format(root_logdir, now)
mnist = input_data.read_data_sets("/tmp/data/")
X_train = mnist.train.images
X_test = mnist.test.images
y_train = mnist.train.labels.astype("int")
y_test = mnist.test.labels.astype("int")
n_inputs = 28*28 # MNIST
n_hidden1 = 300
n_hidden2 = 100
n_hidden3 = 40
n_outputs = 10
learning_rate = 0.01
n_epochs = 20
batch_size = 50
input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input")
y = tf.placeholder(tf.int64, shape=(None), name="y")
def neuron_layer(X, n_neurons, name, activation=None):
with tf.name_scope(name):
n_inputs = int(X.get_shape()[1])
stddev = 2 / np.sqrt(n_inputs)
init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev)
W = tf.Variable(init, name="weights")
b = tf.Variable(tf.zeros([n_neurons]), name="bias")
Z = tf.matmul(X, W) + b
if activation is not None:
return activation(Z)
else:
return Z
def leaky_relu(z, name=None):
return tf.maximum(0.01 * z, z, name=name)
with tf.name_scope("dnn"):
hidden1 = neuron_layer(input, n_hidden1, name="hidden1", activation=leaky_relu)
hidden2 = neuron_layer(hidden1, n_hidden2, name="hidden2", activation=tf.nn.selu)
logits = neuron_layer(hidden2, n_outputs, name="outputs") #, activation=tf.nn.sigmoid)
with tf.name_scope("loss"):
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
loss = tf.reduce_mean(xentropy, name="loss")
with tf.name_scope("train"):
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
training_op = optimizer.minimize(loss)
with tf.name_scope("eval"):
correct = tf.nn.in_top_k(logits, y, 1)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
init = tf.global_variables_initializer()
accuracy_summary = tf.summary.scalar('Accuracy', accuracy)
file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())
with tf.Session() as sess:
init.run()
for epoch in range(n_epochs):
for iteration in range(mnist.train.num_examples // batch_size):
X_batch, y_batch = mnist.train.next_batch(batch_size)
sess.run(training_op, feed_dict={input: X_batch, y: y_batch})
acc_train = accuracy.eval(feed_dict={input: X_batch, y: y_batch})
acc_val = accuracy.eval(feed_dict={input: mnist.validation.images,
y: mnist.validation.labels})
print(epoch, "Train accuracy:", acc_train, "Val accuracy:", acc_val)
# Save summary for tensorboard
summary_str = accuracy_summary.eval(feed_dict={input: mnist.validation.images,
y: mnist.validation.labels})
file_writer.add_summary(summary_str, epoch)
export_path = "saved"
print('Exporting trained model to ', export_path)
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':input}, outputs = {'y':logits})
builder.add_meta_graph_and_variables(sess,
[tf.saved_model.tag_constants.SERVING],
signature_def_map={'serving_default':signature})
builder.save(as_text=True)
file_writer.close()
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