tensorflow 加载计算图

加载模型时要先定义tensorflow计算图上的所有运算

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
import tensorflow as tf
learning_rate=0.001
batch_size=100
display_step=1
model_path='E:/learn/pc_code/tensorflow/1.2/model.ckpt'
n_hidden_1 = 256
n_hidden_2 = 256
n_input = 784
n_classes = 10
x=tf.placeholder(tf.float32,[None,n_input])
y=tf.placeholder(tf.float32,[None,n_classes])
#create model
def multilayer(x, weights, biases):
    layer_1 = tf.nn.relu(tf.matmul(x,weights['h1'])+biases['b1'])
    layer_2 = tf.nn.relu(tf.matmul(layer_1,weights['h2'])+biases['b2'])
    return tf.matmul(layer_2, weights['out'])+biases['out']
weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}

biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

pre=multilayer(x,weights=weights,biases=biases)
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pre,labels=y))
optimizer=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

saver=tf.train.Saver()
with tf.Session() as sess:
    saver.restore(sess, model_path)
    correct_pre=tf.equal(tf.argmax(y,1),tf.argmax(pre,1))
    accuracy = tf.reduce_mean(tf.cast(correct_pre,tf.float32))
    for epoch in range(7):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples / batch_size)
        # Loop over all batches
        for i in range(total_batch):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            # Run optimization op (backprop) and cost op (to get loss value)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                          y: batch_y})
            # Compute average loss
            avg_cost += c / total_batch
        # Display logs per epoch step
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch + 1), "accuracy=",accuracy.eval(
                {x: mnist.test.images, y: mnist.test.labels}) )