Keras入门笔记

最近也是深受某人的影响，决定入坑Keras。在拥有TF经验的基础上，入门keras居然只用了2个小时。

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入坑keras的理由：

1. 实在是简便，如同tf游乐场一样的操作。实现算法的费力程度降低了很多；
2. 支持TF/theano/CNTK为后端框架，作为TFer确实需要更简便的算法实现工具了；
3. 运行速度上，理论上不会下降很多，毕竟后端是基于TF的，而且支持GPU训练；等以后买了TPU，依然可以用到keras上。
4. 代码也简单易读；

毕竟，keras的官方文档上也是这么说:

为快速实验而生，能够把idea迅速转换为成果

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Keras

先丢图：


丢几个我借鉴的链接：
keras中文文档:
http://keras-cn.readthedocs.io/en/latest/
博客：深度学习：Keras入门(一)之基础篇
还有非常好的免费视频学习网站：莫烦主页

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有了以上三个链接，你搞不定keras来找我。

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当然，我这篇博文不是为了推荐学习链接的。那还是有些辅助性干货的，
以下每段代码都可以直接运行(一共有三段基于keras实现的机器学习代码，分别包括回归算法、多层感知机、CNN的基于Keras实现的代码，祝你结合代码搞定Keras！)：

1. 用keras实现二维线性回归算法：

import numpy as np
np.random.seed(1337)  # for reproducibility
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt # 可视化模块

# create some data
X = np.linspace(-1, 1, 200)
np.random.shuffle(X)    # randomize the data
Y = 0.5 * X + 2 + np.random.normal(0, 0.05, (200, ))
# plot data
plt.scatter(X, Y)
plt.show()

X_train, Y_train = X[:160], Y[:160]     # train 前 160 data points
X_test, Y_test = X[160:], Y[160:]       # test 后 40 data points


model = Sequential()
model.add(Dense(output_dim=1, input_dim=1))

# choose loss function and optimizing method
model.compile(loss='mse', optimizer='sgd')

# training
print('Training -----------')
for step in range(301):
    cost = model.train_on_batch(X_train, Y_train)
    if step % 100 == 0:
        print('train cost: ', cost)

"""
Training -----------
train cost:  4.111329555511475
train cost:  0.08777070790529251
train cost:  0.007415373809635639
train cost:  0.003544030711054802
"""

# test
print('\nTesting ------------')
cost = model.evaluate(X_test, Y_test, batch_size=40)
print('test cost:', cost)
W, b = model.layers[0].get_weights()
print('Weights=', W, '\nbiases=', b)

"""
Testing ------------
40/40 [==============================] - 0s
test cost: 0.004269329831
Weights= [[ 0.54246825]] 
biases= [ 2.00056005]
"""

# plotting the prediction
Y_pred = model.predict(X_test)
plt.scatter(X_test, Y_test)
plt.plot(X_test, Y_pred)
plt.show()

1. 用keras实现2层全连接网络(mnist)：

import numpy as np
np.random.seed(1337)  # for reproducibility
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import RMSprop
# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# X shape (60,000 28x28), y shape (10,000, )
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# data pre-processing
X_train = X_train.reshape(X_train.shape[0], -1) / 255.   # normalize
X_test = X_test.reshape(X_test.shape[0], -1) / 255.      # normalize
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

print(X_train[1].shape)
"""
(784,)
"""

print(y_train[:3])
"""
[[ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.]
 [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]]
"""



# Another way to build your neural net
model = Sequential([
    Dense(32, input_dim=784),
    Activation('relu'),
    Dense(10),
    Activation('softmax'),
])

# Another way to define your optimizer
rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)

# We add metrics to get more results you want to see
model.compile(optimizer=rmsprop,
              loss='categorical_crossentropy',
              metrics=['accuracy'])


print('Training ------------')
# Another way to train the model
model.fit(X_train, y_train, epochs=2, batch_size=32)

"""
Training ------------
Epoch 1/2
60000/60000 [==============================] - 2s - loss: 0.3506 - acc: 0.9025     
Epoch 2/2
60000/60000 [==============================] - 2s - loss: 0.1995 - acc: 0.9421   
"""



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)

"""
Testing ------------
 9760/10000 [============================>.] - ETA: 0s

test loss:  0.1724540345
test accuracy:  0.9489
"""

1. 用keras完成CNN，基于mnist数据集：

#!/usr/bin/env python
# encoding: utf-8

from keras.layers import Convolution2D, MaxPooling2D, Flatten
import numpy as np

np.random.seed(1337)  # for reproducibility
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import Adam

# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# X shape (60,000 28x28), y shape (10,000, )
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# data pre-processing
X_train = X_train.reshape(-1, 1, 28, 28)  # normalize
X_test = X_test.reshape(-1, 1, 28, 28)  # normalize
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)
print(X_train.shape, y_train.shape)

model = Sequential()
model.add(Convolution2D(
    input_shape=(1, 28, 28),
    nb_filter=32,
    nb_row = 5,
    nb_col = 5,
    padding='same',  # Padding method
))
model.add(Activation('relu'))

model.add(MaxPooling2D(
    pool_size=2,
    strides=2,
    padding='same',  # Padding method
    data_format='channels_first',
))

model.add(Convolution2D(64, 5, strides=1, padding='same', data_format='channels_first'))
model.add(Activation('relu'))
model.add(MaxPooling2D(2, 2, 'same', data_format='channels_first'))

model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))

model.add(Dense(10))
model.add(Activation('softmax'))

adam = Adam(lr=1e-4)
model.compile(optimizer=adam,
              loss='categorical_crossentropy',
              metrics=['accuracy'])



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)

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从回归算法，到FC，再到CNN。这在学tensorflow的时候，用一个礼拜搞定算快。但是基于keras，一晚上的功夫就搞定了。当然，需要一定的基础。