我正在使用 tflearn 和 gym 编写机器学习脚本。
我能够让一个网络在我的 python 脚本中工作，但每当我尝试调用我的函数来构建第二个或第三个网络并使用 model.fit< 对其进行训练时/strong>，我得到一个
tensorflow.python.framework.errors_impl.InvalidArgumentError
编辑；目标应该是建立几个不同的网络以便对它们进行比较。首先，这应该只关注输入数据和训练时期的数量，但最后，我想比较不同的网络大小。 此外，我想循环运行它，建立两个以上的网络。
以下代码重现了我的错误:
- 初始人口(pop_size)
创建随机操作数组，大小为 pop_size
- neural_network_model(输入大小):
创建神经网络
- train_model(训练数据)
如果没有通过，则创建一个新模型，并根据提供的训练数据训练模型
import gym
import random
import numpy as np
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
LR = 1e-3
env = gym.make('CartPole-v0')
env.reset()
goal_steps = 500
score_requirement = 1
def initial_population(pop_size):
training_data = []
scores = []
accepted_scores = []
for _ in range(pop_size):
score = 0
game_memory = []
prev_observation = []
for _ in range(goal_steps):
action = random.randrange(0,2)
observation, reward, done, info = env.step(action)
if len(prev_observation) > 0:
game_memory.append([prev_observation, action])
prev_observation = observation
score += reward
if done:
break
if score >= score_requirement:
accepted_scores.append(score)
for data in game_memory:
if data[1] == 1:
output = [0,1]
elif data[1] == 0:
output = [1,0]
training_data.append([data[0], output])
env.reset()
scores.append(score)
return np.array(training_data)
def neural_network_model(input_size):
network = input_data(shape=[None, input_size, 1], name='input')
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=LR,
loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(network, tensorboard_dir='log')
return model
def train_model(training_data, model=False, n_training_epochs=5):
X = np.array([i[0] for i in training_data]).reshape(-1, len(training_data[0][0]), 1)
Y = [i[1] for i in training_data]
if not model:
model = neural_network_model(input_size = len(X[0]))
model.fit({'input':X}, {'targets':Y}, n_epoch=n_training_epochs, snapshot_step=500, show_metric=True)
return model
if __name__ == "__main__":
training_data = initial_population(5)
print("still alive 1")
model = train_model(training_data, n_training_epochs=1)
print("still alive 2")
training_data = initial_population(1)
print("still alive 3")
model = train_model(training_data, n_training_epochs=1)
print("still alive 4")
输出:
C:\Users\username\AppData\Local\Programs\Python\Python36\python.exe C:/Users/username/.PyCharm2017.1/config/scratches/scratch.py
curses is not supported on this machine (please install/reinstall curses for an optimal experience)
still alive 1
2017-11-21 01:03:45.096492: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2017-11-21 01:03:45.355914: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Found device 0 with properties:
name: GeForce GTX 980 Ti major: 5 minor: 2 memoryClockRate(GHz): 1.228
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2017-11-21 01:03:45.356242: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 980 Ti, pci bus id: 0000:01:00.0, compute capability: 5.2)
2017-11-21 01:03:46.394283: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 980 Ti, pci bus id: 0000:01:00.0, compute capability: 5.2)
---------------------------------
Run id: BCIV9S
Log directory: log/
---------------------------------
Training samples: 137
Validation samples: 0
--
Training Step: 1 | time: 0.224s
| Adam | epoch: 001 | loss: 0.00000 - acc: 0.0000 -- iter: 064/137
Training Step: 2 | total loss: 0.62389 | time: 0.234s
| Adam | epoch: 001 | loss: 0.62389 - acc: 0.4500 -- iter: 128/137
Training Step: 3 | total loss: 0.68097 | time: 0.239s
| Adam | epoch: 001 | loss: 0.68097 - acc: 0.3631 -- iter: 137/137
--
still alive 2
still alive 3
2017-11-21 01:03:47.234643: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 980 Ti, pci bus id: 0000:01:00.0, compute capability: 5.2)
2017-11-21 01:03:48.302791: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 980 Ti, pci bus id: 0000:01:00.0, compute capability: 5.2)
---------------------------------
Run id: HHBWWQ
Log directory: log/
---------------------------------
Training samples: 20
Validation samples: 0
--
2017-11-21 01:03:49.928408: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\framework\op_kernel.cc:1192] Invalid argument: You must feed a value for placeholder tensor 'input_1/X' with dtype float and shape [?,4,1]
[[Node: input_1/X = Placeholder[dtype=DT_FLOAT, shape=[?,4,1], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
2017-11-21 01:03:49.928684: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\framework\op_kernel.cc:1192] Invalid argument: You must feed a value for placeholder tensor 'input_1/X' with dtype float and shape [?,4,1]
[[Node: input_1/X = Placeholder[dtype=DT_FLOAT, shape=[?,4,1], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
Traceback (most recent call last):
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1323, in _do_call
return fn(*args)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1302, in _run_fn
status, run_metadata)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 473, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'input_1/X' with dtype float and shape [?,4,1]
[[Node: input_1/X = Placeholder[dtype=DT_FLOAT, shape=[?,4,1], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
[[Node: Dropout_1/cond/Merge/_119 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_274_Dropout_1/cond/Merge", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:/Users/username/.PyCharm2017.1/config/scratches/scratch.py", line 69, in <module>
model = train_model(training_data, n_training_epochs=1)
File "C:/Users/username/.PyCharm2017.1/config/scratches/scratch.py", line 58, in train_model
model.fit({'input':X}, {'targets':Y}, n_epoch=n_training_epochs, snapshot_step=500, show_metric=True)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tflearn\models\dnn.py", line 216, in fit
callbacks=callbacks)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tflearn\helpers\trainer.py", line 339, in fit
show_metric)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tflearn\helpers\trainer.py", line 818, in _train
feed_batch)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 889, in run
run_metadata_ptr)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1120, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1317, in _do_run
options, run_metadata)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1336, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'input_1/X' with dtype float and shape [?,4,1]
[[Node: input_1/X = Placeholder[dtype=DT_FLOAT, shape=[?,4,1], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
[[Node: Dropout_1/cond/Merge/_119 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_274_Dropout_1/cond/Merge", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Caused by op 'input_1/X', defined at:
File "C:/Users/username/.PyCharm2017.1/config/scratches/scratch.py", line 69, in <module>
model = train_model(training_data, n_training_epochs=1)
File "C:/Users/username/.PyCharm2017.1/config/scratches/scratch.py", line 57, in train_model
model = neural_network_model(input_size = len(X[0]))
File "C:/Users/username/.PyCharm2017.1/config/scratches/scratch.py", line 44, in neural_network_model
network = input_data(shape=[None, input_size, 1], name='input')
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tflearn\layers\core.py", line 81, in input_data
placeholder = tf.placeholder(shape=shape, dtype=dtype, name="X")
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1599, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 3090, in _placeholder
"Placeholder", dtype=dtype, shape=shape, name=name)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 2956, in create_op
op_def=op_def)
File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1470, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_1/X' with dtype float and shape [?,4,1]
[[Node: input_1/X = Placeholder[dtype=DT_FLOAT, shape=[?,4,1], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
[[Node: Dropout_1/cond/Merge/_119 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_274_Dropout_1/cond/Merge", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Process finished with exit code 1
关键部分似乎是，函数 model.fit 在第二次调用时没有获得正确的数据类型。看起来两个实例可能共享一些变量、数据等，这会搞砸一些事情。
对于常规 tensorflow ，我发现您可能必须为每个新模型执行单独的 session ，但我不知道这是否适用于 tflearn 包。
我正在使用 Windows 10 和 Python 3.6。
最佳答案
实现此功能的一种方法是将 train_model 的第二次调用更改为 train_model(training_data, model, n_training_epochs=1)，以便重用模型它在第一次调用中创建。这似乎并不完全是您想要的，因为您提到尝试建立第二个网络。
在同一 session 中创建第二个模型似乎确实会导致问题，但您可以创建一个模型并使用 model.save 保存它，然后再次运行程序并将另一个模型保存到不同的文件。
从您的问题来看，尚不完全清楚您想要实现的目标，因此我不确定其中任何一个是否适合您。
编辑:好的，我想我已经弄清楚如何做你想做的事了。如果您没有指定要使用哪个图，则 TensorFlow 会将所有内容放入默认图中。您可以指定您希望事物位于单独的图表中，如下所示:
import tensorflow as tf # This can be at the top of the file if you prefer
graph1 = tf.Graph()
with graph1.as_default():
training_data = initial_population(5)
print("still alive 1")
model = train_model(training_data, n_training_epochs=1)
print("still alive 2")
graph2 = tf.Graph()
with graph2.as_default():
training_data = initial_population(1)
print("still alive 3")
model = train_model(training_data, n_training_epochs=1)
print("still alive 4")
关于python - tflearn 创建多个模型，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/47403336/