Keras安装与测试遇到的坑

Keras是基于python的深度学习库

Keras是一个高层神经网络API，Keras由纯Python编写而成并基Tensorflow、Theano以及CNTK后端。

安装步骤及遇到的坑：

（1）安装tensorflow：CMD命令行输入pip install --upgrade tensorflow

（2）安装Keras：pip install keras -U --pre

（3）验证tensorflow

　　jupyter notebook或者spyder输入以下代码：

　　import tensorflow as tf

　　hello = tf.constant(“hello,tensorflow”)

　　sess = tf.Session()

　　print(sess.run(hello))

　　能显示“hello,tensorflow”则表示安装成功

（4）验证keras，

　　使用Keras中mnist数据集测试 下载Keras开发包，命令行输入以下命令

　　>>> conda install git   #安装git工具

　　>>> git clone https://github.com/fchollet/keras.git   #下载keras工程内容

　　>>> cd keras/examples/    #进入测试代码所在路径

　　>>> python mnist_mlp.py   #执行测试代码

验证keras时遇到两个坑，问题描述及解决方案如下：

（1）conda更新失败，安装git工具遇到CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://repo.anaconda.com/pkgs/main/win-64/git-2问题，解决办法是修改国内镜像源，改为清华镜像源即可

>>>conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
>>>conda config --set show_channel_urls yes #生成配置文件

　　修改生成的配置文件 C:Users<你的用户名>.condarc

#修改前
channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
  - default
ssl_verify: true
show_channel_urls: true

#修改后

channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
ssl_verify: true
show_channel_urls: true

　　>>>conda info命令查看配置信息，确认修改成功后，>>>conda install git即可完成下载更新

（2）keras中的example案例中MNIST数据集无法下载

　　问题原因：keras 源码中下载MNIST的方式是 path = get_file(path, origin='https://s3.amazonaws.com/img-datasets/mnist.npz')，数据源是通过 url = https://s3.amazonaws.com/img-datasets/mnist.npz 进行下载的。访问该 url 地址被墙了，导致 MNIST 相关的案例都

　　卡在数据下载部分

　　解决办法：

　　（a）下载好 mnist_npz 数据集，并将其放于 .kerasexamples 目录下

　　（b）修改mnist_mlp.py

'''Trains a simple deep NN on the MNIST dataset.

Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''

from __future__ import print_function

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop

batch_size = 128
num_classes = 10
epochs = 20

#load data from local
import numpy as np
path = "./mnist.npz"
f = np.load(path)
x_train, y_train = f["x_train"], f["y_train"]
x_test, y_test = f["x_test"], f["y_test"]
f.close()

# the data, split between train and test sets
#(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])

history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])