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tflearn創建多個模型

[英]tflearn creating multiple models

 原文  2017-11-21 00:23:51  8   1    python/ machine-learning/ tensorflow/ tflearn/ openai-gym

問題描述

我的工作與tflearn和健身房的機器學習腳本。

我可以在python -script中使用一個網絡，但是每當我嘗試調用函數來構建第二個或第三個網絡並使用model.fit對其進行訓練時 ，我都會得到一個

tensorflow.python.framework.errors_impl.InvalidArgumentError

編輯; 目標應該是建立幾個不同的網絡以進行比較。 首先，這應該只專注於input_data和訓練時期的數量，但是最后，我想比較不同的網絡規模。 另外，我想循環運行它，以建立兩個以上的網絡。

以下代碼重現了我的錯誤：

  • initial_population（pop_size）

創建一個隨機動作數組，其大小為pop_size

  • neural_network_model（input_size）：

創建一個神經網絡

  • train_model（training_data）

如果未通過，則創建一個新模型，並在提供的訓練數據上訓練模型 

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")

隨着輸出： 

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

關鍵部分似乎是，函數model.fit在第二次調用時沒有獲得正確的數據類型。 看來這兩個實例可能共享一些變量，數據等，這弄糟了某些東西。

對於常規的tensorflow，我已經看到您可能必須為每個新模型進行單獨的會話，但是我不知道這是否適用於tflearn包。

我正在Windows 10和Python 3.6上工作。

1 個解決方案

解決方案1
 1  2017-11-21 06:59:21

使其工作的一種方法是train_model的第二次調用train_model為train_model(training_data, model, n_training_epochs=1) ，以便其重用在第一次調用中創建的模型。 這似乎並不是您想要的，因為您提到嘗試建立第二個網絡。

在同一會話中創建第二個模型似乎確實會引起問題，但是您可以創建一個模型並使用model.save保存它，然后再次運行程序並將另一個模型保存到另一個文件中。

從您的問題來看，您尚不清楚要完成什么，所以我不確定這兩種方法是否對您有用。

編輯：好的，我想我已經弄清楚了如何做你想做的。 如果您未指定要使用的圖形，那么TensorFlow會將所有內容放入默認圖形。 您可以指定要讓事物放在單獨的圖中，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")

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