import tensorflow as tf
import numpy as np
import tensorflow.contrib.layers as layers
from tensorflow.python import debug as tf_debug
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("mnist",one_hot=True)
def ProcessLayers(input):
fc1=layers.fully_connected(input,30,activation_fn=tf.nn.relu,scope='fc1')
fc2=layers.fully_connected(fc1,256,activation_fn=tf.nn.relu,scope='fc2')
out=layers.fully_connected(fc2,10,activation_fn=None,scope='out')
return out
x=tf.placeholder(tf.float32,shape=[None,784],name="x")
y=tf.placeholder(tf.float32,shape=[None,10],name="y")
y1=ProcessLayers(x)
entroy=tf.nn.softmax_cross_entropy_with_logits(logits=y1,labels=y)
loss=tf.reduce_mean(entroy)
correct_pred = tf.equal(tf.argmax(y1, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
optimizer=tf.train.AdamOptimizer(learning_rate=0.01)
train_step=optimizer.minimize(loss)
init=tf.global_variables_initializer()
batch_size=200
epochs=int(mnist.train.num_examples/batch_size)
with tf.Session() as sess:
sess.run(init)
"""
x_data = {'images': mnist.train.images}['images']
y_data = mnist.train.labels
"""
x_data_test = {'images': mnist.test.images}['images']
y_data_test = mnist.test.labels
for nStep in range(100):
for n in range(epochs):
batch_x,batch_y=mnist.train.next_batch(batch_size)
sess.run(train_step,feed_dict={x:batch_x,y:batch_y})
lost=sess.run(loss,feed_dict={x:batch_x,y:batch_y})
acc=sess.run(accuracy,feed_dict={x:batch_x,y:batch_y})
test = sess.run(accuracy, feed_dict={x: x_data_test[:256], y: y_data_test[:256]})
print("epoch:%2d,lost:%.2f,acc:%.2f,test:%.2f"%(nStep,lost,acc,test))
------------------------
epoch: 0,lost:0.17,acc:0.94,test:0.97
epoch: 1,lost:0.06,acc:0.98,test:0.97
epoch: 2,lost:0.15,acc:0.96,test:0.98
epoch: 3,lost:0.09,acc:0.96,test:0.97
epoch: 4,lost:0.06,acc:0.98,test:0.98
epoch: 5,lost:0.07,acc:0.98,test:0.96
epoch: 6,lost:0.06,acc:0.98,test:0.97
epoch: 7,lost:0.07,acc:0.98,test:0.96
epoch: 8,lost:0.04,acc:1.00,test:0.98
epoch: 9,lost:0.08,acc:0.97,test:0.95
epoch:10,lost:0.04,acc:0.98,test:0.96
epoch:11,lost:0.05,acc:0.99,test:0.96
epoch:12,lost:0.07,acc:0.96,test:0.97
epoch:13,lost:0.05,acc:0.98,test:0.97
epoch:14,lost:0.07,acc:0.97,test:0.96
epoch:15,lost:0.10,acc:0.98,test:0.97
