序列模型Sequential是层的线性堆叠
可以通过将一个层列表传递到构建器的方式创建Sequential
from keras.models import Sequential from keras.layers import Dense, Activation model = Sequential([ Dense(32, input_shape=(784,)), Activation('relu'), Dense(10), Activation('softmax'), ]) 也可以通过.add()方法增加层 model = Sequential() model.add(Dense(32, input_dim=784)) model.add(Activation('relu')) 明确输入形状模型需要知道预期的输入形状，因此Sequential模型的第一层（只需第一层，因为后面的层能自动计算形状）需要收到输入形状的信息。有几种方式可以实现：
*将input_shape申明传入第一层。这是一个形状元组（整数或None，None意味可能是任意正整数），这里不包含批次维度。
*一些2D层如Dense，通过申明input_dim支持指明输入形状，一些3D的时序层支持申明input_dim和input_length。
*如果需要指明固定的输入批次规模（对状态循环网络有用），可以将batch_size申明传入一个层。如果传入batch_size=32和input_shape(6, 8)至一个层，它将期望所有输入批次形状为（32，6， 8）。
下列片断意义相同：
model = Sequential() model.add(Dense(32, input_shape=(784,))) model = Sequential() model.add(Dense(32, input_dim=784)) 编译训练模型前应设置学习进程，通过compile方法实现，它接受3个申明：
*优化器，可以是现成的优化器如rmsprop或者adagrad，或者是自定义Optimizer类的实例。
*损失函数，模型要最小化的对象，可以是现成的如categorical_crossentropy或者mse，或者自定义。
*度量列表，对于分类问题我们使用metrics=['accuracy']，可以是其它现成度量或自定义。
# For a multi-class classification problem 多类分类问题 model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) # For a binary classification problem 二分问题 model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) # For a mean squared error regression problem 平均平方差回归问题 model.compile(optimizer='rmsprop', loss='mse') # For custom metrics 自定义度量 import keras.backend as K def mean_pred(y_true, y_pred): return K.mean(y_pred) model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy', mean_pred]) 训练keras模型在输入数据和标签的Numpy数组上训练，对于训练模型一般使用fit。
# For a single-input model with 2 classes (binary classification): 二分问题 model = Sequential() model.add(Dense(32, activation='relu', input_dim=100)) model.add(Dense(1, activation='sigmoid')) model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) # Generate dummy data 生成简单试验数据 import numpy as np data = np.random.random((1000, 100)) labels = np.random.randint(2, size=(1000, 1)) # Train the model, iterating on the data in batches of 32 samples 训练模型 model.fit(data, labels, epochs=10, batch_size=32) # For a single-input model with 10 classes (categorical classification): 多分类问题 model = Sequential() model.add(Dense(32, activation='relu', input_dim=100)) model.add(Dense(10, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) # Generate dummy data 生成试验数据 import numpy as np data = np.random.random((1000, 100)) labels = np.random.randint(10, size=(1000, 1)) # Convert labels to categorical one-hot encoding 将标签转化成类 one_hot_labels = keras.utils.to_categorical(labels, num_classes=10) # Train the model, iterating on the data in batches of 32 samples 训练模型 model.fit(data, one_hot_labels, epochs=10, batch_size=32)一些有用的例子
MLP 多层感知机二分问题
import numpy as np from keras.models import Sequential from keras.layers import Dense, Dropout # Generate dummy data x_train = np.random.random((1000, 20)) y_train = np.random.randint(2, size=(1000, 1)) x_test = np.random.random((100, 20)) y_test = np.random.randint(2, size=(100, 1)) model = Sequential() model.add(Dense(64, input_dim=20, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(64, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) model.fit(x_train, y_train, epochs=20, batch_size=128) score = model.evaluate(x_test, y_test, batch_size=128) VGG-类卷积网络 import numpy as np import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.optimizers import SGD # Generate dummy data x_train = np.random.random((100, 100, 100, 3)) y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10) x_test = np.random.random((20, 100, 100, 3)) y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10) model = Sequential() # input: 100x100 images with 3 channels -> (100, 100, 3) tensors. # this applies 32 convolution filters of size 3x3 each. model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3))) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax')) sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd) model.fit(x_train, y_train, batch_size=32, epochs=10) score = model.evaluate(x_test, y_test, batch_size=32) LSTM 序列分类 from keras.models import Sequential from keras.layers import Dense, Dropout from keras.layers import Embedding from keras.layers import LSTM model = Sequential() model.add(Embedding(max_features, output_dim=256)) model.add(LSTM(128)) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) model.fit(x_train, y_train, batch_size=16, epochs=10) score = model.evaluate(x_test, y_test, batch_size=16) 有1D卷积的序列分类 from keras.models import Sequential from keras.layers import Dense, Dropout from keras.layers import Embedding from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D model = Sequential() model.add(Conv1D(64, 3, activation='relu', input_shape=(seq_length, 100))) model.add(Conv1D(64, 3, activation='relu')) model.add(MaxPooling1D(3)) model.add(Conv1D(128, 3, activation='relu')) model.add(Conv1D(128, 3, activation='relu')) model.add(GlobalAveragePooling1D()) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) model.fit(x_train, y_train, batch_size=16, epochs=10) score = model.evaluate(x_test, y_test, batch_size=16)堆叠LSTM 时序分类
这里我们堆叠3个LSTM层，使模型能学习高级时序表征。
前两层LSTM返回全部输出序列，但最后一层只返回输出序列最后一步，舍弃时序维度（将输入序列转化为单一向量）
from keras.models import Sequential from keras.layers import LSTM, Dense import numpy as np data_dim = 16 timesteps = 8 num_classes = 10 # expected input data shape: (batch_size, timesteps, data_dim) model = Sequential() model.add(LSTM(32, return_sequences=True, input_shape=(timesteps, data_dim))) # returns a sequence of vectors of dimension 32 model.add(LSTM(32, return_sequences=True)) # returns a sequence of vectors of dimension 32 model.add(LSTM(32)) # return a single vector of dimension 32 model.add(Dense(10, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) # Generate dummy training data x_train = np.random.random((1000, timesteps, data_dim)) y_train = np.random.random((1000, num_classes)) # Generate dummy validation data x_val = np.random.random((100, timesteps, data_dim)) y_val = np.random.random((100, num_classes)) model.fit(x_train, y_train, batch_size=64, epochs=5, validation_data=(x_val, y_val)) 状态堆叠LSTM模型状态循环模型是内部状态（记忆）由处理一批次样本后重复利用作为下一批次的初始状态得到。这使处理更长的序列并保持卷积复杂度可控成为可能。
from keras.models import Sequential from keras.layers import LSTM, Dense import numpy as np data_dim = 16 timesteps = 8 num_classes = 10 batch_size = 32 # Expected input batch shape: (batch_size, timesteps, data_dim) # Note that we have to provide the full batch_input_shape since the network is stateful. # the sample of index i in batch k is the follow-up for the sample i in batch k-1. model = Sequential() model.add(LSTM(32, return_sequences=True, stateful=True, batch_input_shape=(batch_size, timesteps, data_dim))) model.add(LSTM(32, return_sequences=True, stateful=True)) model.add(LSTM(32, stateful=True)) model.add(Dense(10, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) # Generate dummy training data x_train = np.random.random((batch_size * 10, timesteps, data_dim)) y_train = np.random.random((batch_size * 10, num_classes)) # Generate dummy validation data x_val = np.random.random((batch_size * 3, timesteps, data_dim)) y_val = np.random.random((batch_size * 3, num_classes)) model.fit(x_train, y_train, batch_size=batch_size, epochs=5, shuffle=False, validation_data=(x_val, y_val))