Tensorflow - 恢復模型

Question

我有以下代碼，我試圖在代碼中的某個點恢復模型，但似乎我得到了一些無限循環（不確定），因爲程序不會盡管似乎正在運行，但返回任何輸出：

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') 

可能是什麼問題？我在這裏恢復模型嗎？

謝謝。

Answer 1

看來我不得不把整個路徑列表的模型如下：

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

時節約模型，如圖所示我犯同樣的錯誤here :-)