python - tflearn 创建多个模型

Question

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。

Answer 1

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")