Tensorflow-恢复模型

Question

I have the following code, where I'm trying to restore the model at some point in the code, but seems that I'm getting some infinite loop (not sure), as the program would not return any output although seems to be running:

import tensorflow as tf

data, labels = cifar_tools.read_data('C:\\Users\\abc\\Desktop\\Testing')

x = tf.placeholder(tf.float32, [None, 150 * 150])
y = tf.placeholder(tf.float32, [None, 2])

w1 = tf.Variable(tf.random_normal([5, 5, 1, 64]))
b1 = tf.Variable(tf.random_normal([64]))

w2 = tf.Variable(tf.random_normal([5, 5, 64, 64]))
b2 = tf.Variable(tf.random_normal([64]))

w3 = tf.Variable(tf.random_normal([38*38*64, 1024]))
b3 = tf.Variable(tf.random_normal([1024]))

w_out = tf.Variable(tf.random_normal([1024, 2]))
b_out = tf.Variable(tf.random_normal([2]))

def conv_layer(x,w,b):
    conv = tf.nn.conv2d(x,w,strides=[1,1,1,1], padding = 'SAME')
    conv_with_b = tf.nn.bias_add(conv,b)
    conv_out = tf.nn.relu(conv_with_b)
    return conv_out

def maxpool_layer(conv,k=2):
    return tf.nn.max_pool(conv, ksize=[1,k,k,1], strides=[1,k,k,1], padding='SAME')

def model():
    x_reshaped = tf.reshape(x, shape=[-1, 150, 150, 1])

    conv_out1 = conv_layer(x_reshaped, w1, b1)
    maxpool_out1 = maxpool_layer(conv_out1)
    norm1 = tf.nn.lrn(maxpool_out1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
    conv_out2 = conv_layer(norm1, w2, b2)
    norm2 = tf.nn.lrn(conv_out2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
    maxpool_out2 = maxpool_layer(norm2)

    maxpool_reshaped = tf.reshape(maxpool_out2, [-1, w3.get_shape().as_list()[0]])
    local = tf.add(tf.matmul(maxpool_reshaped, w3), b3)
    local_out = tf.nn.relu(local)

    out = tf.add(tf.matmul(local_out, w_out), b_out)
    return out

model_op = model()

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(model_op, y))
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)

correct_pred = tf.equal(tf.argmax(model_op, 1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    onehot_labels = tf.one_hot(labels, 2, on_value=1.,off_value=0.,axis=-1)
    onehot_vals = sess.run(onehot_labels)
    batch_size = len(data)
    # Restore model
    saver = tf.train.import_meta_graph('mymodel.meta')
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    all_vars = tf.get_collection('vars')
    for v in all_vars:
        v_ = sess.run(v)
        print(v_)

for j in range(0, 5):
    print('EPOCH', j)
    for i in range(0, len(data), batch_size):
        batch_data = data[i:i+batch_size, :]
        batch_onehot_vals = onehot_vals[i:i+batch_size, :]
        _, accuracy_val = sess.run([train_op, accuracy], feed_dict={x: batch_data, y: batch_onehot_vals})
        print(i, accuracy_val)

    print('DONE WITH EPOCH')

What could be the issue? Am I restoring the model correct here?

Thanks.

Answer 1

It seems I had to list the whole path to the model as follows:

saver = tf.train.import_meta_graph('C:\\Users\\abc\\Desktop\\\Testing\\mymodel.meta')

The same mistake I made when saving the model, as shown here :-)