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import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
import tensorflow.contrib.layers as layers
from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('./data/mnist_data', one_hot=True)batch_size = 200
eta = 0.001
max_epoch = 50
n_hidden = 30
n_classes = 10
n_input = 784def multilayer_perceptron(x):
fc1 = layers.fully_connected(x, n_hidden, activation_fn=tf.nn.relu)
out = layers.fully_connected(fc1, n_classes, activation_fn=None)
return outx = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
y_hat = multilayer_perceptron(x)loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_hat, labels=y))
train = tf.train.AdamOptimizer(learning_rate=eta).minimize(loss)
correct_prediction = tf.equal(tf.argmax(y_hat,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, dtype=tf.float32))
init = tf.global_variables_initializer()with tf.Session() as sess:
sess.run(init)
for epoch in range(max_epoch):
epoch_loss = 0.0
batch_steps = int(mnist.train.num_examples/batch_size)
for i in range(batch_steps):
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, c = sess.run([train,loss], feed_dict={x:batch_x, y:batch_y})
epoch_loss += c / batch_steps
accur = sess.run(accuracy,feed_dict={x:mnist.test.images, y:mnist.test.labels})
print('Epoch %02d, Loss = %.6f, Accuracy = %.6f' %(epoch, epoch_loss, accur))
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