吴裕雄 python深度学习与实践（15）

import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data

mnist = input_data.read_data_sets("D:\F\TensorFlow_deep_learn\MNIST\", one_hot=True)

x_data = tf.placeholder("float32", [None, 784])
weight = tf.Variable(tf.ones([784, 10]))
bias = tf.Variable(tf.ones([10]))
y_model = tf.nn.softmax(tf.matmul(x_data, weight) + bias)
y_data = tf.placeholder("float32", [None, 10])

loss = tf.reduce_sum(tf.pow((y_model - y_data), 2))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

for _ in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x_data:batch_xs, y_data:batch_ys})
    if _ % 50 == 0:
        correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
        print(sess.run(accuracy, feed_dict={x_data: mnist.test.images, y_data: mnist.test.labels}))

import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data

mnist = input_data.read_data_sets("D:\F\TensorFlow_deep_learn\MNIST\", one_hot=True)

x_data = tf.placeholder("float32", [None, 784])
weight = tf.Variable(tf.ones([784, 10]))
bias = tf.Variable(tf.ones([10]))
y_model = tf.nn.relu(tf.matmul(x_data, weight) + bias)
y_data = tf.placeholder("float32", [None, 10])
loss = -tf.reduce_sum(y_data*tf.log(y_model))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

for _ in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(50)
    sess.run(train_step, feed_dict={x_data:batch_xs, y_data:batch_ys})
    if _ % 50 == 0:
        correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
        print(sess.run(accuracy, feed_dict={x_data: mnist.test.images, y_data: mnist.test.labels}))

import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data

mnist = input_data.read_data_sets("D:\F\TensorFlow_deep_learn\MNIST\", one_hot=True)

x_data = tf.placeholder("float32", [None, 784])

weight1 = tf.Variable(tf.ones([784, 256]))
bias1 = tf.Variable(tf.ones([256]))
y1_model1 = tf.matmul(x_data, weight1) + bias1

weight2 = tf.Variable(tf.ones([256, 10]))
bias2 = tf.Variable(tf.ones([10]))
y_model = tf.nn.softmax(tf.matmul(y1_model1, weight2) + bias2)

y_data = tf.placeholder("float32", [None, 10])

loss = -tf.reduce_sum(y_data*tf.log(y_model))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

for _ in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(50)
    sess.run(train_step, feed_dict={x_data:batch_xs, y_data:batch_ys})
    if _ % 50 == 0:
        correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
        print(sess.run(accuracy, feed_dict={x_data: mnist.test.images, y_data: mnist.test.labels}))

import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data

mnist = input_data.read_data_sets("D:\F\TensorFlow_deep_learn\MNIST\", one_hot=True)

x_data = tf.placeholder("float32", [None, 784])
x_image = tf.reshape(x_data, [-1,28,28,1])

w_conv = tf.Variable(tf.ones([5,5,1,32]))
b_conv = tf.Variable(tf.ones([32]))
h_conv = tf.nn.relu(tf.nn.conv2d(x_image, w_conv, strides=[1, 1, 1, 1], padding='SAME') + b_conv)

h_pool = tf.nn.max_pool(h_conv, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

w_fc = tf.Variable(tf.ones([14*14*32,1024]))
b_fc = tf.Variable(tf.ones([1024]))

h_pool_flat = tf.reshape(h_pool, [-1, 14*14*32])
h_fc = tf.nn.relu(tf.matmul(h_pool_flat, w_fc) + b_fc)

W_fc2 = tf.Variable(tf.ones([1024,10]))
b_fc2 = tf.Variable(tf.ones([10]))

y_model = tf.nn.softmax(tf.matmul(h_fc, W_fc2) + b_fc2)

y_data = tf.placeholder("float32", [None, 10])

loss = -tf.reduce_sum(y_data*tf.log(y_model))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

for _ in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(200)
    sess.run(train_step, feed_dict={x_data:batch_xs, y_data:batch_ys})
    if _ % 50 == 0:
        correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
        print(sess.run(accuracy, feed_dict={x_data: mnist.test.images, y_data: mnist.test.labels}))

import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data

mnist = input_data.read_data_sets("D:\F\TensorFlow_deep_learn\MNIST\", one_hot=True)

x_data = tf.placeholder("float", shape=[None, 784])
y_data = tf.placeholder("float", shape=[None, 10])


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)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='VALID')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')


W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x_data, [-1, 28, 28, 1]) 
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1) 

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) 

W_fc1 = weight_variable([4 * 4 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 4*4*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

cross_entropy = -tf.reduce_sum(y_data * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-2).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_data, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

sess = tf.Session()
sess.run(tf.initialize_all_variables())

for i in range(1000):
    batch = mnist.train.next_batch(50)
    if i%5 == 0:
        train_accuracy = sess.run(accuracy, feed_dict={x_data:batch[0], y_data: batch[1], keep_prob: 1.0})
        print("step %d, training accuracy %g"%(i, train_accuracy))
    sess.run(train_step, feed_dict={x_data: batch[0], y_data: batch[1], keep_prob: 0.5})