python - 当我尝试运行此文件时出现错误。tf 学习问题？

Question

我发现了一些我想从 GitHub 运行的东西，但是当我这样做时，我得到了这个错误。

Traceback (most recent call last):
  File "C:\Users\Samuel\Desktop\GTA\pygta5-master\pygta5-master\Tutorial Codes\Part 14-15\train_model.py", line 4, in <module>
    from alexnet import alexnet
  File "C:\Users\Samuel\Desktop\GTA\pygta5-master\pygta5-master\Tutorial Codes\Part 14-15\alexnet.py", line 11, in <module>
    import tflearn
  File "C:\Users\Samuel\AppData\Local\Programs\Python\Python36\lib\site-packages\tflearn\__init__.py", line 4, in <module>
    from . import config
  File "C:\Users\Samuel\AppData\Local\Programs\Python\Python36\lib\site-packages\tflearn\config.py", line 3, in <module>
    import tensorflow as tf
  File "C:\Users\Samuel\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\__init__.py", line 22, in <module>
    from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
  File "C:\Users\Samuel\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\__init__.py", line 52, in <module>
    from tensorflow.core.framework.graph_pb2 import *
  File "C:\Users\Samuel\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\core\framework\graph_pb2.py", line 6, in <module>
    from google.protobuf import descriptor as _descriptor
  File "C:\Users\Samuel\AppData\Local\Programs\Python\Python36\lib\site-packages\google\protobuf\descriptor.py", line 47, in <module>
    from google.protobuf.pyext import _message
ImportError: DLL load failed: The specified procedure could not be found.

这是代码

# train_model.py

import numpy as np
from alexnet import alexnet
WIDTH = 160
HEIGHT = 120
LR = 1e-3
EPOCHS = 10
MODEL_NAME = 'pygta5-car-fast-{}-{}-{}-epochs-300K-data.model'.format(LR, 'alexnetv2',EPOCHS)

model = alexnet(WIDTH, HEIGHT, LR)

hm_data = 22
for i in range(EPOCHS):
    for i in range(1,hm_data+1):
        train_data = np.load('training_data-{}-balanced.npy'.format(i))

        train = train_data[:-100]
        test = train_data[-100:]

        X = np.array([i[0] for i in train]).reshape(-1,WIDTH,HEIGHT,1)
        Y = [i[1] for i in train]

        test_x = np.array([i[0] for i in test]).reshape(-1,WIDTH,HEIGHT,1)
        test_y = [i[1] for i in test]

        model.fit({'input': X}, {'targets': Y}, n_epoch=1, validation_set=({'input': test_x}, {'targets': test_y}), 
            snapshot_step=500, show_metric=True, run_id=MODEL_NAME)

        model.save(MODEL_NAME)



# tensorboard --logdir=foo:C:/path/to/log

看起来它试图访问 alexnet.py 这是

# alexnet.py

""" AlexNet.
References:
    - Alex Krizhevsky, Ilya Sutskever & Geoffrey E. Hinton. ImageNet
    Classification with Deep Convolutional Neural Networks. NIPS, 2012.
Links:
    - [AlexNet Paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
"""

import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
from tflearn.layers.normalization import local_response_normalization

def alexnet(width, height, lr):
    network = input_data(shape=[None, width, height, 1], name='input')
    network = conv_2d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 256, 5, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 3, activation='softmax')
    network = regression(network, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=lr, name='targets')

    model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                        max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')

    return model

环顾四周后，我尝试了很多东西，然后我厌倦了在 python cmd 上导入 tflearn，当我运行命令 import tflearn 时它不会导入。那么这是一个 Tflearn 问题吗？

我在 python 3.6 上，这里安装了其他所有东西

absl-py             0.5.0
astor               0.7.1
gast                0.2.0
grpcio              1.15.0
h5py                2.8.0
Keras-Applications  1.0.6
Keras-Preprocessing 1.0.5
Markdown            3.0.1
numpy               1.15.2
opencv-python       3.4.3.18
pandas              0.23.4
Pillow              5.3.0
pip                 18.0
protobuf            3.6.1
pypiwin32           223
python-dateutil     2.7.3
pytz                2018.5
pywin32             224
setuptools          28.8.0
six                 1.11.0
tensorboard         1.11.0
tensorflow          1.11.0
tensorflow-gpu      1.11.0
termcolor           1.1.0
tflearn             0.3.2
virtualenv          16.0.0
Werkzeug            0.14.1
wheel               0.32.0

标签： pythonpython-3.xtensorflowtflearn