Python实现Keras搭建神经网络训练CNN模型
2020-06-07 05:55
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[code]# CNN example
import numpy as np
# for reproducibility
np.random.seed(1337)
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten
from keras.optimizers import Adam
# 程序中用到的数据是经典的手写体识别mnist数据集
# download the mnist to the path if it is the first time to be called
# X shape (60,000 28x28), y
# 下载minst.npz：
# 链接: https://pan.baidu.com/s/1b2ppKDOdzDJxivgmyOoQsA
# 提取码: y5ir
# 将下载好的minst.npz放到当前目录下
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()
# data pre-processing
# -1代表例子的个数 ，1是channel
# 数组新的shape属性应该要与原来的配套，
# 如果等于-1的话，那么Numpy会根据剩下的维度计算出数组的另外一个shape属性值
# 这里是将一组图像矩阵x重建为新的矩阵，该新矩阵的维数为（a, 1, 28, 28），其中-1表示a由实际情况来定
# 例如，一组图像的矩阵（假设是50张，大小为56×56），
# 则执行reshape(-1, 1, 28, 28)
# 可以计算a = 50×56×56/1/28/28 = 200
# 即维数为（200, 1, 28, 28）
X_train = X_train.reshape(-1, 1, 28, 28) / 255
X_test = X_test.reshape(-1, 1, 28, 28) / 255
y_train = np_utils.to_categorical(y_train, num_classes = 10)
y_test = np_utils.to_categorical(y_test, num_classes = 10)
# Another way to build your CNN
model = Sequential()
# Conv layer 1 output shape (32, 28, 28)
model.add(Conv2D(
filters=32,
kernel_size=5,
# padding method
padding='same',
# （batch, channels, steps）第一个参数是batch，1是channel，后面的是height & width
batch_input_shape=(None, 1, 28, 28),
# 有两种格式，根据输入格式来定这个
# channels_first对应（batch, channels, steps）
# channels_last对应（batch, steps, channels）
data_format='channels_first'
))
model.add(Activation('relu'))
# Pooling layer 1 (max pooling) output shape (32, 14, 14)
model.add(MaxPooling2D(
pool_size=2,
strides=2,
# padding method
padding='same',
data_format='channels_first'
))
# Conv layer 2 output shape (64, 14, 14)
model.add(Conv2D(64, 5, strides=1, padding='same', data_format='channels_first'))
model.add(Activation('relu'))
# Pooling layer 2 (max pooling) output shape(64, 7, 7)
model.add(MaxPooling2D(2, 2, padding='same', data_format='channels_first'))
# Fully connected layer 1 input shape (64 * 7 * 7) = (3136), output shape (1024)
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
# Fully connected layer 2 to shape (10) for 10 classes
model.add(Dense(10))
model.add(Activation('softmax'))
# Another way to define your optimizer
adam = Adam(lr=1e-4)
# We add metrics to get more results you want to see
model.compile(
optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy']
)
print('Training------------')
# Another way to train the model
model.fit(X_train, y_train, epochs=1, batch_size=32)
print('\nTesting------------')
# Evaluate the model with the metrics we defined earlier
loss, accuracy = model.evaluate(X_test, y_test)
print('test loss:', loss)
print('test accuracy:', accuracy)
运行结果：
[code]Using TensorFlow backend. Training------------ Epoch 1/1 32/60000 [..............................] - ETA: 17:51 - loss: 2.3100 - accuracy: 0.1250 64/60000 [..............................] - ETA: 12:46 - loss: 2.2977 - accuracy: 0.1562 96/60000 [..............................] - ETA: 11:09 - loss: 2.2815 - accuracy: 0.2083 128/60000 [..............................] - ETA: 10:17 - loss: 2.2717 - accuracy: 0.2266 160/60000 [..............................] - ETA: 9:55 - loss: 2.2680 - accuracy: 0.2438 192/60000 [..............................] - ETA: 9:32 - loss: 2.2564 - accuracy: 0.2708 224/60000 [..............................] - ETA: 9:24 - loss: 2.2360 - accuracy: 0.3170 256/60000 [..............................] - ETA: 9:19 - loss: 2.2269 - accuracy: 0.3281 288/60000 [..............................] - ETA: 9:10 - loss: 2.2209 - accuracy: 0.3333 320/60000 [..............................] - ETA: 9:03 - loss: 2.2121 - accuracy: 0.3375 352/60000 [..............................] - ETA: 8:56 - loss: 2.2113 - accuracy: 0.3295 384/60000 [..............................] - ETA: 8:53 - loss: 2.1961 - accuracy: 0.3490 416/60000 [..............................] - ETA: 8:54 - loss: 2.1883 - accuracy: 0.3486 448/60000 [..............................] - ETA: 8:48 - loss: 2.1729 - accuracy: 0.3616 480/60000 [..............................] - ETA: 8:43 - loss: 2.1591 - accuracy: 0.3771 512/60000 [..............................] - ETA: 8:42 - loss: 2.1469 - accuracy: 0.3867 544/60000 [..............................] - ETA: 8:41 - loss: 2.1389 - accuracy: 0.3952 576/60000 [..............................] - ETA: 8:38 - loss: 2.1257 - accuracy: 0.4097 608/60000 [..............................] - ETA: 8:35 - loss: 2.1105 - accuracy: 0.4227 640/60000 [..............................] - ETA: 8:30 - loss: 2.0995 - accuracy: 0.4359 672/60000 [..............................] - ETA: 8:28 - loss: 2.0867 - accuracy: 0.4494 704/60000 [..............................] - ETA: 8:27 - loss: 2.0733 - accuracy: 0.4673 736/60000 [..............................] - ETA: 8:22 - loss: 2.0550 - accuracy: 0.4837 768/60000 [..............................] - ETA: 8:18 - loss: 2.0473 - accuracy: 0.4935 800/60000 [..............................] - ETA: 8:15 - loss: 2.0362 - accuracy: 0.5000 832/60000 [..............................] - ETA: 8:12 - loss: 2.0273 - accuracy: 0.5060 864/60000 [..............................] - ETA: 8:11 - loss: 2.0132 - accuracy: 0.5150 896/60000 [..............................] - ETA: 8:10 - loss: 2.0020 - accuracy: 0.5190 928/60000 [..............................] - ETA: 8:07 - loss: 1.9858 - accuracy: 0.5269 960/60000 [..............................] - ETA: 8:05 - loss: 1.9712 - accuracy: 0.5312 992/60000 [..............................] - ETA: 8:02 - loss: 1.9586 - accuracy: 0.5363 1024/60000 [..............................] - ETA: 8:00 - loss: 1.9408 - accuracy: 0.5410 1056/60000 [..............................] - ETA: 7:59 - loss: 1.9297 - accuracy: 0.5445 1088/60000 [..............................] - ETA: 7:57 - loss: 1.9131 - accuracy: 0.5496 1120/60000 [..............................] - 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accuracy: 0.6316 1856/60000 [..............................] - ETA: 7:30 - loss: 1.5693 - accuracy: 0.6336 1888/60000 [..............................] - ETA: 7:30 - loss: 1.5530 - accuracy: 0.6372 1920/60000 [..............................] - ETA: 7:28 - loss: 1.5360 - accuracy: 0.6406 1952/60000 [..............................] - ETA: 7:28 - loss: 1.5243 - accuracy: 0.6429 1984/60000 [..............................] - ETA: 7:27 - loss: 1.5119 - accuracy: 0.6442 2016/60000 [>.............................] - ETA: 7:26 - loss: 1.4997 - accuracy: 0.6468 2048/60000 [>.............................] - ETA: 7:26 - loss: 1.4885 - accuracy: 0.6484 2080/60000 [>.............................] - ETA: 7:25 - loss: 1.4755 - accuracy: 0.6519 2112/60000 [>.............................] - ETA: 7:25 - loss: 1.4656 - accuracy: 0.6539 2144/60000 [>.............................] - ETA: 7:24 - loss: 1.4523 - accuracy: 0.6558 2176/60000 [>.............................] - ETA: 7:24 - loss: 1.4414 - accuracy: 0.6576 2208/60000 [>.............................] - 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