我已經訓練了一個模型並保存了檢查點。我的模型的代碼是:Tensorflow,如何添加一些新圖層時恢復模型?
with tf.variable_scope(scope):
self.inputs = tf.placeholder(shape=[None, 80, 80, 1], dtype=tf.float32)
self.conv_1 = slim.conv2d(activation_fn=tf.nn.relu, inputs=self.inputs, num_outputs=32,
kernel_size=[8, 8], stride=4, padding='SAME')
self.conv_2 = slim.conv2d(activation_fn=tf.nn.relu, inputs=self.conv_1, num_outputs=64,
kernel_size=[4, 4], stride=2, padding='SAME')
self.conv_3 = slim.conv2d(activation_fn=tf.nn.relu, inputs=self.conv_2, num_outputs=64,
kernel_size=[3, 3], stride=1, padding='SAME')
self.fc = slim.fully_connected(slim.flatten(self.conv_3), 512, activation_fn=tf.nn.elu)
# Output layers for policy and value estimations
self.policy = slim.fully_connected(self.fc,
cfg.ACTION_DIM,
activation_fn=tf.nn.softmax,
biases_initializer=None)
self.value = slim.fully_connected(self.fc,
1,
activation_fn=None,
biases_initializer=None)
有同時運行約32處理,並且每個具有在上面的代碼中定義的全局網絡的一個副本,該scope
是每個過程的ID。全球網絡的scope
是global
。
之後,我想在self.fc
圖層之後添加更多圖層。
with tf.variable_scope(scope):
self.inputs = tf.placeholder(shape=[None, 80, 80, 1], dtype=tf.float32)
self.conv_1 = slim.conv2d(activation_fn=tf.nn.relu, inputs=self.inputs, num_outputs=32,
kernel_size=[8, 8], stride=4, padding='SAME')
self.conv_2 = slim.conv2d(activation_fn=tf.nn.relu, inputs=self.conv_1, num_outputs=64,
kernel_size=[4, 4], stride=2, padding='SAME')
self.conv_3 = slim.conv2d(activation_fn=tf.nn.relu, inputs=self.conv_2, num_outputs=64,
kernel_size=[3, 3], stride=1, padding='SAME')
self.fc = slim.fully_connected(slim.flatten(self.conv_3), 512, activation_fn=tf.nn.elu)
# Output layers for policy and value estimations
self.policy = slim.fully_connected(self.fc,
cfg.ACTION_DIM,
activation_fn=tf.nn.softmax,
biases_initializer=None)
self.value = slim.fully_connected(self.fc,
1,
activation_fn=None,
biases_initializer=None)
self.new_fc_1 = slim.fully_connected(self.fc, 512, activation_fn=tf.nn.elu)
然而,當我恢復模型,它報告了以下錯誤:
2017-08-03 22:23:43.473157: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key worker_15/fully_connected_3/weights not found in checkpoint
2017-08-03 22:23:43.477197: W tensorflow/core/framework/op_kernel.cc:1158] Data loss: Checksum does not match: stored 379803423 vs. calculated on the restored bytes 2648422677
2017-08-03 22:23:43.477210: W tensorflow/core/framework/op_kernel.cc:1158] Data loss: Checksum does not match: stored 3963326522 vs. calculated on the restored bytes 3154501583
2017-08-03 22:23:43.477200: W tensorflow/core/framework/op_kernel.cc:1158] Data loss: Checksum does not match: stored 3893236466 vs. calculated on the restored bytes 1767411214
2017-08-03 22:23:43.478276: W tensorflow/core/framework/op_kernel.cc:1158] Data loss: Checksum does not match: stored 4239176201 vs. calculated on the restored bytes 3213118706
2017-08-03 22:23:43.480438: W tensorflow/core/framework/op_kernel.cc:1158] Data loss: Checksum does not match: stored 442335910 vs. calculated on the restored bytes 4248164641
2017-08-03 22:23:43.483885: W tensorflow/core/framework/op_kernel.cc:1158] Data loss: Checksum does not match: stored 3105262865 vs. calculated on the restored bytes 2648422677
2017-08-03 22:23:43.483953: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key worker_15/fully_connected_3/weights not found in checkpoint
[[Node: save/RestoreV2_128 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_128/tensor_names, save/RestoreV2_128/shape_and_slices)]]
2017-08-03 22:23:43.486987: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key worker_15/fully_connected_3/weights not found in checkpoint
[[Node: save/RestoreV2_128 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_128/tensor_names, save/RestoreV2_128/shape_and_slices)]]
2017-08-03 22:23:43.490616: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key worker_15/fully_connected_3/weights not found in checkpoint
[[Node: save/RestoreV2_128 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_128/tensor_names, save/RestoreV2_128/shape_and_slices)]]
2017-08-03 22:23:43.491951: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key worker_15/fully_connected_3/weights not found in checkpoint
[[Node: save/RestoreV2_128 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_128/tensor_names, save/RestoreV2_128/shape_and_slices)]]
2017-08-03 22:23:43.491957: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key worker_15/fully_connected_3/weights not found in checkpoint
[[Node: save/RestoreV2_128 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_128/tensor_names, save/RestoreV2_128/shape_and_slices)]]
2017-08-03 22:23:43.494310: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key worker_15/fully_connected_3/weights not found in checkpoint
[[Node: save/RestoreV2_128 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_128/tensor_names, save/RestoreV2_128/shape_and_slices)]]
.... ....
我用下面的代碼保存模型
saver.save(sess, self.model_path+'/model-'+str(episode_count)+'.ckpt')
這裏是定義代碼金丹
value_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='global/old_scope')
value_list.extend(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='global/actor_critic'))
saver = tf.train.Saver(value_list, max_to_keep=100)
with tf.Session(config=tf_configs) as sess:
coord = tf.train.Coordinator()
if load_model:
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(model_path)
saver.restore(sess, ckpt.model_checkpoint_path)
else:
sess.run(tf.global_variables_initializer())
當一些具有隨機初始化參數的新層添加到當前神經網絡中時,如何恢復預先訓練的模型?
使用舊模型恢復檢查點並在之後添加新張量 – aseipel