Windows下基于pycharm进行Keras配置

目录

 

1.下载安装PyCharm

2.安装Python环境

3.下载配置Keras库

4.程序测试

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1.下载安装PyCharm

下载地址：https://www.baidu.com/link?url=d7pDJKNJFN-8vn2JKoBIKUEJIfObFDkbLY16ONHZIPtdG1joUb0-El8nTNVlnV2o&ck=3408.4.55.400.311.169.209.336&shh=www.baidu.com&sht=02049043_62_pg&wd=&eqid=8f2494e00014f991000000035d03821c

2.安装Python环境

安装的时候勾选上环境变量配置

下载安装Anaconda，地址：

https://repo.continuum.io/archive/index.html

3.下载配置Keras库

全部安装完成后，打开pycharm软件，设置

安装完成后

同理，安装keras库

配置完成。

4.程序测试

# -*- coding:utf-8 -*-
import numpy as np

np.random.seed(123)
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D
from keras.layers import Embedding, LSTM
from keras.utils import np_utils
from keras.datasets import mnist


# 载入mnist数据集(第一次执行需要下载数据)
def loda_mnist():
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    # 5. Preprocess input data
    X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
    X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
    X_train = X_train.astype('float32')
    X_test = X_test.astype('float32')
    X_train /= 255
    X_test /= 255
    Y_train = np_utils.to_categorical(y_train, 10)
    Y_test = np_utils.to_categorical(y_test, 10)
    print
    X_train.shape, Y_train.shape
    print
    X_test.shape, Y_test.shape
    return X_train, Y_train, X_test, Y_test


if __name__ == '__main__':
    X_train, Y_train, X_test, Y_test = loda_mnist();
    model = Sequential()
    # 2个卷积层
    model.add(Conv2D(32, (5, 5), activation='relu', input_shape=(28, 28, 1)))  # 第一层卷积
    model.add(MaxPooling2D(pool_size=(2, 2)))  # 第一层池化
    model.add(Conv2D(64, (5, 5), activation='relu', input_shape=(14, 14, 1)))  # 第二层卷积
    model.add(MaxPooling2D(pool_size=(2, 2)))  # 第二层池化
    model.add(Dropout(0.25))  # 添加节点keep_prob
    # 2个全连接层
    model.add(Flatten())  # 将多维数据压成1维，方便全连接层操作
    model.add(Dense(1024, activation='relu'))  # 添加全连接层
    model.add(Dropout(0.5))
    model.add(Dense(10, activation='softmax'))
    # 编译模型
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    # 训练模型
    model.fit(X_train, Y_train, batch_size=32, nb_epoch=10, verbose=1)
    # 评估模型
    score = model.evaluate(X_test, Y_test, verbose=0)
    print(score)

输出

Using TensorFlow backend.
Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz

    8192/11490434 [..............................] - ETA: 15:57
   24576/11490434 [..............................] - ETA: 10:43
   40960/11490434 [..............................] - ETA: 8:05 
   57344/11490434 [..............................] - ETA: 6:50
   73728/11490434 [..............................] - ETA: 6:08
  106496/11490434 [..............................] - ETA: 4:52
  122880/11490434 [..............................] - ETA: 4:46

……



59616/60000 [============================>.] - ETA: 0s - loss: 0.0179 - acc: 0.9948
59680/60000 [============================>.] - ETA: 0s - loss: 0.0180 - acc: 0.9948
59744/60000 [============================>.] - ETA: 0s - loss: 0.0180 - acc: 0.9948
59808/60000 [============================>.] - ETA: 0s - loss: 0.0180 - acc: 0.9948
59872/60000 [============================>.] - ETA: 0s - loss: 0.0180 - acc: 0.9948
59936/60000 [============================>.] - ETA: 0s - loss: 0.0180 - acc: 0.9948
60000/60000 [==============================] - 74s 1ms/step - loss: 0.0181 - acc: 0.9948
[0.03161891764163324, 0.9914]

Process finished with exit code 0

最简单的手写数字数据集识别网络，99% 的正确率，恭喜，到此，你已经学会了深度学习了。