莫烦TensorFlow_11 MNIST优化使用CNN
2024-09-02 07:44:51
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data #number 1 to 10 data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) def compute_accuracy(v_xs, v_ys):
global prediction
y_pre = sess.run(prediction, feed_dict={xs:v_xs, keep_prob:1})
correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
result = sess.run(accuracy, feed_dict={xs:v_xs, ys:v_ys, keep_prob:1})
return result def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1) # initial variables with normal distribution
return tf.Variable(initial) def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial) def conv2d(x, W):
#strides [1, x_movement, y_movement, 1]
#Must have strides[0] = strides[3] = 1
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding = 'SAME') def max_pool_2x2(x):
#strides [1, x_movement, y_movement, 1]
#Must have strides[0] = strides[3] = 1
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides = [1,2,2,1], padding = 'SAME') #define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 784])
ys = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs, [-1, 28, 28, 1])
#print(x_image.shape) #[n_sample, 28, 28, 1] ## conv1 layer ##
W_conv1 = weight_variable([5,5,1,32])#patch 5x5, in in size 1, out size 32
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32
h_pool1 = max_pool_2x2(h_conv1)# output size 14x14x32 ## conv2 layer ##
W_conv2 = weight_variable([5,5,32, 64])#patch 5x5, in in size 32, out size 64
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64
h_pool2 = max_pool_2x2(h_conv2)# output size 7x7x64 ## func1 layer ##
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
# [n_sample, 7,7,64] ->> [n_sample, 7*7*64]
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)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) ## func2 layer ##
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10]) prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) # the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),
reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) sess = tf.Session()
sess.run(tf.global_variables_initializer()) for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict = {xs:batch_xs, ys:batch_ys, keep_prob:0.8})
if i% 50 == 0:
print(compute_accuracy(mnist.test.images, mnist.test.labels))
两层卷积层
训练速度慢了,但是精度提高了
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