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import tensorflow as tf
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import numpy as np
import pandas as pd
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n_inputs = 28 * 28
n_hidden1 = 300
n_hidden2 = 100
n_outputs = 10
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X = tf.placeholder(dtype=tf.float32, shape=(None, n_inputs), name='X')
y = tf.placeholder(dtype=tf.int64, shape=(None), name='y')
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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='weight')
b = tf.Variable(tf.zeros([n_neurons]), name='biases')
z = tf.matmul(X, W) + b
if activation == 'relu':
return tf.nn.relu(z)
else:
return z
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with tf.name_scope("dnn"):
hidden1 = neuron_layer(X, n_hidden1, 'hidden1', activation='relu')
hidden2 = neuron_layer(hidden1, n_hidden2, 'hidden2', activation='relu')
logits = neuron_layer(hidden2, n_outputs, 'outputs')
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from tensorflow.contrib.layers import fully_connected
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with tf.name_scope('dnn'):
hidden1 = fully_connected(X, n_hidden1, scope='hidden1')
hidden2 = fully_connected(hidden1, n_hidden2, scope='hidden2')
logits = fully_connected(hidden2, n_outputs, scope='outputs', activation_fn=None)
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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")
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learning_rate = 0.01
with tf.name_scope('train'):
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(loss)
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with tf.name_scope('eval'):
correct = tf.nn.in_top_k(logits, y, 1)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
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init = tf.global_variables_initializer()
saver = tf.train.Saver()
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from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('/tmp/data/')
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n_epochs = 400
batch_size = 50
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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=batch_size)
sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
acc_test = accuracy.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels})
print(epoch, "Train accuracy:", acc_train, "Test accuracy:", acc_test)
save_path = saver.save(sess, './my_model_final.ckpt')
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