Question

我有以下代码正常工作（没有错误）。我的问题是我是否正确恢复模型？特别是我看不到语句print(v_)的任何输出。

所以，我想知道我是否正确地做以下事情：

恢复模型

使用已恢复的模型

将tensorflow导入为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('C:\\Users\\abc\\Desktop\\\Testing\\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')

编辑1

恢复这种方式会有效吗？

saver = tf.train.Saver()
saver = tf.train.import_meta_graph('C:\\Users\\Abder-Rahman\\Desktop\\\Testing\\mymodel.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))
print('model restored'

编辑2

这就是我保存我的模型的方式：

#Save model
saver = tf.train.Saver()
saved_path = saver.save(sess, 'C:\\Users\\abc\\Desktop\\\Testing\\mymodel')
print("The model is in this file: ", saved_path)

感谢。

Answer 1

您的保护代码是正确的。虽然变量必须在检索集合之前添加到集合中。 tf.add_to_collection("vars", w1) tf.add_to_collection("vars", b1) ... 然后 all_vars = tf.get_collection('vars')

Answer 2

通常我会像这样恢复TensorFlow模型：

 with tf.Session(graph=graph) as session:
    if os.path.exists(save_path):
        # Restore variables from disk.
        saver.restore(session, save_path)
    else:
        tf.initialize_all_variables().run()
        print('Initialized')

    # do the work
    # ... 
 saver.save(session, save_path)   # save the model

示例代码可以是fetch here。

我需要了解更多关于如何保存模型的信息，似乎您的模型在保存之前已经恢复，而且您的模型没有转到tf.graph并与会话连接。

Answer 3

我假设您已经阅读了我的博客here，模型保存的机制非常简单，当您加载模型时，参数值和关系（可能是您关心的）可以通过变量名匹配

例如

all_vars = tf.get_collection('vars')

让我困惑的是，您使用了函数tf.all_variables()，但您从未定义过名为“vars”的范围。您可能应首先使用{{1}}进行测试。

Tensorflow - 我是否正确恢复了模型？

3 个答案: