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import tensorflow as tf
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from tensorflow.examples.tutorials.mnist import input_data
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mnist = input_data.read_data_sets('/tmp/tensorflow/mnist/input_data', one_hot=True)
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x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
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def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
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def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
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W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
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x_image = tf.reshape(x, [-1,28,28,1])
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h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
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W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
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W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
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keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
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W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
y_pred = tf.nn.softmax(y_conv)
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sess = tf.Session()
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cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.global_variables_initializer())
for i in range(200):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
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prediction=tf.argmax(y_pred,1)
prediction_val = prediction.eval(feed_dict={x: mnist.test.images[0: 10], keep_prob: 1.0}, session=sess)
print("predictions", prediction_val)
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probabilities=y_pred
probabilities_val = probabilities.eval(feed_dict={x: mnist.test.images[0: 10], keep_prob: 1.0}, session=sess)
print ("probabilities", probabilities_val)
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import matplotlib.pylab as plt
%matplotlib inline
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img = mnist.test.images[0]
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img.shape
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import numpy as np
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for i in range(0, 10):
img = mnist.test.images[i]
print('correct label:', np.argmax(mnist.test.labels[i]))
print('predict label:', prediction_val[i])
print('predict prob:', np.max(probabilities_val[i]))
plt.imshow(img.reshape([28, 28]), cmap=plt.cm.binary)
plt.show()
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model_path = './MNIST.ckpt'
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save_path = tf.train.Saver().save(sess, model_path)
print ("Model saved in file: ", save_path)
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with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
tf.train.Saver().restore(sess, model_path)
prediction=tf.argmax(y_pred,1)
prediction_val = prediction.eval(feed_dict={x: mnist.test.images[0: 10], keep_prob: 1.0}, session=sess)
print("predictions", prediction_val)
probabilities=y_pred
probabilities_val = probabilities.eval(feed_dict={x: mnist.test.images[0: 10], keep_prob: 1.0}, session=sess)
print ("probabilities", probabilities_val)
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