私はtflearnとgymを使用して機械学習スクリプトに取り組んでいます。
python -scriptで 1 つのネットワークを動作させることができますが、関数を呼び出して 2 番目または 3 番目のネットワークを構築し、それをmodel.fitでトレーニングしようとするたびに、
tensorflow.python.framework.errors_impl.InvalidArgumentError
編集; 目標は、それらを比較するためにいくつかの異なるネットワークを構築することです。まず、これは input_data とトレーニング エポックの数だけに注目する必要がありますが、最後に、さまざまなネットワーク サイズを比較したいと思います。さらに、ループで実行して、2 つ以上のネットワークを構築したいと考えています。
次のコードは私のエラーを再現します:
- initial_population(pop_size)
pop_size のサイズのランダムなアクションの配列を作成します
- ニューラル ネットワーク モデル (input_size):
ニューラルネットワークを作成します
- train_model(トレーニング_データ)
何も渡されない場合は新しいモデルを作成し、提供されたトレーニング データでモデルをトレーニングします
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")
出力で：
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
重要な部分は、関数model.fitが2回目に呼び出されたときに正しいデータ型を取得しないことです。両方のインスタンスがいくつかの変数、データなどを共有しているように見え、それが何かを台無しにします。
通常の tensorflow の場合、新しいモデルごとに個別のセッションを実行する必要がある場合があることがわかりましたが、それが tflearn パッケージに当てはまるかどうかはわかりません。
私は Windows 10 と Python 3.6 で作業しています。