Keras 回归神经网络

• Sequential 按顺序建立
• Dense 全连接层

# please note, all tutorial code are running under python3.5.
# If you use the version like python2.7, please modify the code accordingly

# 4 - Regressor example

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

# 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]     # first 160 data points
X_test, Y_test = X[160:], Y[160:]       # last 40 data points

# build a neural network from the 1st layer to the last layer
model = Sequential()
model.add(Dense(units=1, input_dim=1)) # 一层隐藏层

# choose loss function and optimizing method
model.compile(loss='mse', optimizer='sgd')  # 优化器：Stochastic Gradient Descent 随机梯度下降

# 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)

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

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

分类神经网络

# please note, all tutorial code are running under python3.5.
# If you use the version like python2.7, please modify the code accordingly

# 5 - Classifier example

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) # one hot encoding
y_test = np_utils.to_categorical(y_test, num_classes=10)

# Another way to build your neural net
model = Sequential([
    Dense(32, input_dim=784),  # 784压到32个layer
    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) #自定义optimizer

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

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

CNN

# please note, all tutorial code are running under python3.5.
# If you use the version like python2.7, please modify the code accordingly

# 6 - CNN example

# to try tensorflow, un-comment following two lines
# import os
# os.environ['KERAS_BACKEND']='tensorflow'

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, Convolution2D, MaxPooling2D, Flatten
from keras.optimizers import Adam

# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# training X shape (60000, 28x28), Y shape (60000, ). test X shape (10000, 28x28), Y shape (10000, )
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# data pre-processing
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(Convolution2D(
    batch_input_shape=(None, 1, 28, 28),
    filters=32,
    kernel_size=5,
    strides=1,
    padding='same',     # Padding method
    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='same',    # Padding method
    data_format='channels_first',
))

# Conv layer 2 output shape (64, 14, 14)
model.add(Convolution2D(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, '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=64,)

print('\nTesting ------------')
# Evaluate the model with the metrics we defined earlier
loss, accuracy = model.evaluate(X_test, y_test)

print('\ntest loss: ', loss)
print('\ntest accuracy: ', accuracy)

RNN

# please note, all tutorial code are running under python3.5.
# If you use the version like python2.7, please modify the code accordingly

# 8 - RNN Classifier example

# to try tensorflow, un-comment following two lines
# import os
# os.environ['KERAS_BACKEND']='tensorflow'

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 SimpleRNN, Activation, Dense
from keras.optimizers import Adam

TIME_STEPS = 28     # same as the height of the image
INPUT_SIZE = 28     # same as the width of the image
BATCH_SIZE = 50
BATCH_INDEX = 0
OUTPUT_SIZE = 10
CELL_SIZE = 50
LR = 0.001


# 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, 28, 28) / 255.      # normalize
X_test = X_test.reshape(-1, 28, 28) / 255.        # normalize
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

# build RNN model
model = Sequential()

# RNN cell
model.add(SimpleRNN(
    # for batch_input_shape, if using tensorflow as the backend, we have to put None for the batch_size.
    # Otherwise, model.evaluate() will get error.
    batch_input_shape=(None, TIME_STEPS, INPUT_SIZE),       # Or: input_dim=INPUT_SIZE, input_length=TIME_STEPS,
    output_dim=CELL_SIZE,
    unroll=True,
))

# output layer
model.add(Dense(OUTPUT_SIZE))
model.add(Activation('softmax'))

# optimizer
adam = Adam(LR)
model.compile(optimizer=adam,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# training
for step in range(4001):
    # data shape = (batch_num, steps, inputs/outputs)
    X_batch = X_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :, :]
    Y_batch = y_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :]
    cost = model.train_on_batch(X_batch, Y_batch)
    BATCH_INDEX += BATCH_SIZE
    BATCH_INDEX = 0 if BATCH_INDEX >= X_train.shape[0] else BATCH_INDEX

    if step % 500 == 0:
        cost, accuracy = model.evaluate(X_test, y_test, batch_size=y_test.shape[0], verbose=False)
        print('test cost: ', cost, 'test accuracy: ', accuracy)

LSTM

# please note, all tutorial code are running under python3.5.
# If you use the version like python2.7, please modify the code accordingly

# 8 - RNN LSTM Regressor example

# to try tensorflow, un-comment following two lines
# import os
# os.environ['KERAS_BACKEND']='tensorflow'
import numpy as np
np.random.seed(1337)  # for reproducibility
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import LSTM, TimeDistributed, Dense
from keras.optimizers import Adam

BATCH_START = 0
TIME_STEPS = 20
BATCH_SIZE = 50
INPUT_SIZE = 1
OUTPUT_SIZE = 1
CELL_SIZE = 20
LR = 0.006


def get_batch():
    global BATCH_START, TIME_STEPS
    # xs shape (50batch, 20steps)
    xs = np.arange(BATCH_START, BATCH_START+TIME_STEPS*BATCH_SIZE).reshape((BATCH_SIZE, TIME_STEPS)) / (10*np.pi)
    seq = np.sin(xs)
    res = np.cos(xs)
    BATCH_START += TIME_STEPS
    # plt.plot(xs[0, :], res[0, :], 'r', xs[0, :], seq[0, :], 'b--')
    # plt.show()
    return [seq[:, :, np.newaxis], res[:, :, np.newaxis], xs]

model = Sequential()
# build a LSTM RNN
model.add(LSTM(
    batch_input_shape=(BATCH_SIZE, TIME_STEPS, INPUT_SIZE),       # Or: input_dim=INPUT_SIZE, input_length=TIME_STEPS,
    output_dim=CELL_SIZE,
    return_sequences=True,      # True: output at all steps. False: output as last step.
    stateful=True,              # True: the final state of batch1 is feed into the initial state of batch2
))
# add output layer
model.add(TimeDistributed(Dense(OUTPUT_SIZE)))
adam = Adam(LR)
model.compile(optimizer=adam,
              loss='mse',)

print('Training ------------')
for step in range(501):
    # data shape = (batch_num, steps, inputs/outputs)
    X_batch, Y_batch, xs = get_batch()
    cost = model.train_on_batch(X_batch, Y_batch)
    pred = model.predict(X_batch, BATCH_SIZE)
    plt.plot(xs[0, :], Y_batch[0].flatten(), 'r', xs[0, :], pred.flatten()[:TIME_STEPS], 'b--')
    plt.ylim((-1.2, 1.2))
    plt.draw()
    plt.pause(0.1)
    if step % 10 == 0:
        print('train cost: ', cost)