Keras入门——（1）全连接神经网络FCN
Anaconda安装Keras：
conda install keras
安装完成：
在Jupyter Notebook中新建并执行代码：
import keras from keras.datasets import mnist # 从keras中导入mnist数据集 from keras.models import Sequential # 导入序贯模型 from keras.layers import Dense # 导入全连接层 from keras.optimizers import SGD # 导入优化函数 (x_train, y_train), (x_test, y_test) = mnist.load_data() # 加载mnist数据集
因为众所周知的原因，下载墙外的文件超时报错，参考 https://www.cnblogs.com/shinny/p/9283372.html 进行修改；
重复执行，报错：“TabError: inconsistent use of tabs and spaces in indentation”
参照 https://blog.csdn.net/qq_41096996/article/details/85947560 进行修改：
执行成功！
继续执行如下代码：
print(x_train.shape,y_train.shape) #(60000, 28, 28) (60000,) print(x_test.shape,y_test.shape) #(10000, 28, 28) (10000,)
继续执行：
import matplotlib.pyplot as plt # 导入可视化的包 im = plt.imshow(x_train[0],cmap='gray')
继续执行：
plt.show() y_train[0]
继续执行：
x_train = x_train.reshape(60000,784) # 将图片摊平，变成向量 x_test = x_test.reshape(10000,784) # 对测试集进行同样的处理 print(x_train.shape) #(60000, 784) print(x_test.shape) #(10000, 784)
继续执行：
x_train[0]
#array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 18, 18, 18,
126, 136, 175, 26, 166, 255, 247, 127, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170, 253,
253, 253, 253, 253, 225, 172, 253, 242, 195, 64, 0, 0, 0,
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253, 253, 253, 253, 253, 251, 93, 82, 82, 56, 39, 0, 0,
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253, 253, 253, 253, 198, 182, 247, 241, 0, 0, 0, 0, 0,
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80, 156, 107, 253, 253, 205, 11, 0, 43, 154, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 16, 93, 252, 253, 187,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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253, 249, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 46, 130,
183, 253, 253, 207, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 148,
229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114,
221, 253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 66,
213, 253, 253, 253, 253, 198, 81, 2, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 171,
219, 253, 253, 253, 253, 195, 80, 9, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 172,
226, 253, 253, 253, 253, 244, 133, 11, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
136, 253, 253, 253, 212, 135, 132, 16, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0], dtype=uint8)
继续执行：
x_train = x_train / 255
x_test = x_test / 255
x_train[0]
#array([0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
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0.07058824, 0.49411765, 0.53333333, 0.68627451, 0.10196078,
0.65098039, 1. , 0.96862745, 0.49803922, 0. ,
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0. , 0. , 0. , 0. , 0. ,
0. , 0.11764706, 0.14117647, 0.36862745, 0.60392157,
0.66666667, 0.99215686, 0.99215686, 0.99215686, 0.99215686,
0.99215686, 0.88235294, 0.6745098 , 0.99215686, 0.94901961,
0.76470588, 0.25098039, 0. , 0. , 0. ,
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0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686,
0.99215686, 0.99215686, 0.99215686, 0.98431373, 0.36470588,
0.32156863, 0.32156863, 0.21960784, 0.15294118, 0. ,
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0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.71372549,
0.96862745, 0.94509804, 0. , 0. , 0. ,
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0.31372549, 0.61176471, 0.41960784, 0.99215686, 0.99215686,
0.80392157, 0.04313725, 0. , 0.16862745, 0.60392157,
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0. , 0. , 0. , 0. , 0.05490196,
0.00392157, 0.60392157, 0.99215686, 0.35294118, 0. ,
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0. , 0. , 0. , 0. , 0.54509804,
0.99215686, 0.74509804, 0.00784314, 0. , 0. ,
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0.2745098 , 0. , 0. , 0. , 0. ,
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0.42352941, 0.00392157, 0. , 0. , 0. ,
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0.31764706, 0.94117647, 0.99215686, 0.99215686, 0.46666667,
0.09803922, 0. , 0. , 0. , 0. ,
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0.72941176, 0.99215686, 0.99215686, 0.58823529, 0.10588235,
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0.98823529, 0.99215686, 0.73333333, 0. , 0. ,
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0.97647059, 0.25098039, 0. , 0. , 0. ,
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0.71764706, 0.99215686, 0.99215686, 0.81176471, 0.00784314,
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0.58039216, 0.89803922, 0.99215686, 0.99215686, 0.99215686,
0.98039216, 0.71372549, 0. , 0. , 0. ,
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0.99215686, 0.99215686, 0.78823529, 0.30588235, 0. ,
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0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.31764706,
0.00784314, 0. , 0. , 0. , 0. ,
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0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.76470588,
0.31372549, 0.03529412, 0. , 0. , 0. ,
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0.88627451, 0.99215686, 0.99215686, 0.99215686, 0.99215686,
0.95686275, 0.52156863, 0.04313725, 0. , 0. ,
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继续执行：
y_train = keras.utils.to_categorical(y_train,10) y_test = keras.utils.to_categorical(y_test,10)
继续执行：
model = Sequential() # 构建一个空的序贯模型 # 添加神经网络层 model.add(Dense(512,activation='relu',input_shape=(784,))) model.add(Dense(256,activation='relu')) model.add(Dense(10,activation='softmax')) model.summary()
返回信息：
WARNING:tensorflow:From C:\3rd\Anaconda2\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 512) 401920 _________________________________________________________________ dense_2 (Dense) (None, 256) 131328 _________________________________________________________________ dense_3 (Dense) (None, 10) 2570 ================================================================= Total params: 535,818 Trainable params: 535,818 Non-trainable params: 0 _________________________________________________________________
继续执行：
model.compile(optimizer=SGD(),loss='categorical_crossentropy',metrics=['accuracy']) model.fit(x_train,y_train,batch_size=64,epochs=5,validation_data=(x_test,y_test)) # 此处直接将测试集用作了验证集
返回正在执行信息：
执行结束信息：
WARNING:tensorflow:From C:\3rd\Anaconda2\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. Train on 60000 samples, validate on 10000 samples Epoch 1/5 60000/60000 [==============================] - 7s 123us/step - loss: 0.7558 - acc: 0.8162 - val_loss: 0.3672 - val_acc: 0.8991 Epoch 2/5 60000/60000 [==============================] - 7s 112us/step - loss: 0.3356 - acc: 0.9068 - val_loss: 0.2871 - val_acc: 0.9204 Epoch 3/5 60000/60000 [==============================] - 7s 112us/step - loss: 0.2798 - acc: 0.9211 - val_loss: 0.2537 - val_acc: 0.9296 Epoch 4/5 60000/60000 [==============================] - 7s 117us/step - loss: 0.2468 - acc: 0.9302 - val_loss: 0.2313 - val_acc: 0.9332 Epoch 5/5 60000/60000 [==============================] - 7s 122us/step - loss: 0.2228 - acc: 0.9378 - val_loss: 0.2084 - val_acc: 0.9404 <keras.callbacks.History at 0x1dcaea054a8>
继续执行：
score = model.evaluate(x_test,y_test) #10000/10000 [==============================] - 1s 53us/step
继续执行：
print("loss:",score[0])
#loss: 0.2084256855905056
继续执行：
print("accu:",score[1])
#accu: 0.9404
参考：
https://www.cnblogs.com/ncuhwxiong/p/9836648.html
https://www.cnblogs.com/shinny/p/9283372.html
https://blog.csdn.net/qq_41096996/article/details/85947560
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