我想加载一个预训练模型并使用该模型进行继续训练。
保存模型的标准代码段(pretrain.py
):
tf.reset_default_graph()
# tf Graph input
X = tf.placeholder("float", [None, n_input])
Y = tf.placeholder("float", [None, n_classes])
mlp_layer_name = ['h1', 'b1', 'h2', 'b2', 'h3', 'b3', 'w_o', 'b_o']
logits = multilayer_perceptron(X, n_input, n_classes, mlp_layer_name)
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y), name='loss_op')
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op, name='train_op')
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
# Loop over all batches
for i in range(total_batch):
batch_x, batch_y = next(train_generator)
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([train_op, loss_op], feed_dict={X: batch_x,
Y: batch_y})
# Compute average loss
avg_cost += c / total_batch
print("Epoch: {:3d}, cost = {:.6f}".format(epoch+1, avg_cost))
print("Optimization Finished!")
saver.save(sess, 'model')
print("Model saved")
现在加载预训练模型并使用它进行进一步训练(
continue.py
)。# tf Graph input
X = tf.placeholder("float", [None, n_input])
Y = tf.placeholder("float", [None, n_classes])
mlp_layer_name = ['h1', 'b1', 'h2', 'b2', 'h3', 'b3', 'w_o', 'b_o']
logits = multilayer_perceptron(X, n_input, n_classes, mlp_layer_name)
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y), name='loss_op')
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op, name='train_op')
with tf.Session() as sess:
saver = tf.train.import_meta_graph('model.meta')
saver.restore(sess, tf.train.latest_checkpoint('./')) # search for checkpoint file
graph = tf.get_default_graph()
for epoch in range(training_epochs):
avg_cost = 0.
# Loop over all batches
for i in range(total_batch):
batch_x, batch_y = next(train_generator)
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([train_op, loss_op], feed_dict={X: batch_x,
Y: batch_y})
# Compute average loss
avg_cost += c / total_batch
print("Epoch: {:3d}, cost = {:.6f}".format(epoch+1, avg_cost))
但是它显示以下错误:
tensorflow.python.framework.errors_impl.FailedPreconditionError: 尝试使用未初始化的值h1 [[节点:h1 / read = IdentityT = DT_FLOAT,_class = [“loc:@ h1”],_device =“/ job:localhost / replica:0 / task:0 / cpu: 0”]]
以下是我的问题: 1. 在许多TensorFlow教程中,它使用
get_tensor_by_name()
来加载权重和偏差。在这里,我不想获得权重和偏差。我只想加载模型并继续训练。
2. 错误显示张量未初始化。但是,我认为saver.restore(sess,tf.train.latest_checkpoint('./'))
应该已经成功加载了权重和偏差。
如果需要说明我的问题,请看下面的multilayer_perceptron()
。def multilayer_perceptron(x, n_input, n_classes, name):
n_hidden_1 = 512
n_hidden_2 = 256
n_hidden_3 = 128
# Store layers weight & bias
weights = {
'h1' : tf.get_variable(name[0], initializer=tf.random_normal([n_input, n_hidden_1])),
'h2' : tf.get_variable(name[2], initializer=tf.random_normal([n_hidden_1, n_hidden_2])),
'h3' : tf.get_variable(name[4], initializer=tf.random_normal([n_hidden_2, n_hidden_3])),
'w_o': tf.get_variable(name[6], initializer=tf.random_normal([n_hidden_3, n_classes]))
}
biases = {
'b1' : tf.get_variable(name[1], initializer=tf.random_normal([n_hidden_1])),
'b2' : tf.get_variable(name[3], initializer=tf.random_normal([n_hidden_2])),
'b3' : tf.get_variable(name[5], initializer=tf.random_normal([n_hidden_3])),
'b_o': tf.get_variable(name[7], initializer=tf.random_normal([n_classes]))
}
layer_1 = tf.nn.relu(tf.add(tf.matmul(x , weights['h1']), biases['b1']))
layer_1 = tf.layers.dropout(layer_1, rate=0.5, training=True)
layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))
layer_2 = tf.layers.dropout(layer_2, rate=0.3, training=True)
layer_3 = tf.nn.relu(tf.add(tf.matmul(layer_2, weights['h3']), biases['b3']))
layer_3 = tf.layers.dropout(layer_3, rate=0.1, training=True)
out_layer = tf.matmul(layer_3, weights['w_o']) + biases['b_o']
return out_layer