开始 Keras 序列模型(Sequential model)
来源:互联网 发布:淘宝店铺投诉电话 编辑:程序博客网 时间:2024/06/14 11:03
开始 Keras 序列模型(Sequential model)
序列模型是一个线性的层次堆栈。
你可以通过传递一系列 layer 实例给构造器来创建一个序列模型。
The Sequential
model is a linear stack of layers.
You can create a Sequential
model by passing a list of layer instances to the constructor:
from keras.models import Sequentialfrom keras.layers import Dense, Activationmodel = Sequential([ Dense(32, input_shape=(784,)), Activation('relu'), Dense(10), Activation('softmax'),])
也可以简单的添加 layer 通过 .add() 函数。
You can also simply add layers via the .add()
method:
model = Sequential()model.add(Dense(32, input_dim=784))model.add(Activation('relu'))
Specifying the input shape
The model needs to know what input shape it should expect. For this reason, the first layer in a Sequential
model (and only the first, because following layers can do automatic shape inference) needs to receive information about its input shape. There are several possible ways to do this:
- Pass an
input_shape
argument to the first layer. This is a shape tuple (a tuple of integers orNone
entries, whereNone
indicates that any positive integer may be expected). Ininput_shape
, the batch dimension is not included. - Some 2D layers, such as
Dense
, support the specification of their input shape via the argumentinput_dim
, and some 3D temporal layers support the argumentsinput_dim
andinput_length
. - If you ever need to specify a fixed batch size for your inputs (this is useful for stateful recurrent networks), you can pass a
batch_size
argument to a layer. If you pass bothbatch_size=32
andinput_shape=(6, 8)
to a layer, it will then expect every batch of inputs to have the batch shape(32, 6, 8)
.
As such, the following snippets are strictly equivalent:
model = Sequential()model.add(Dense(32, input_shape=(784,)))
model = Sequential()model.add(Dense(32, input_dim=784))
Compilation
Before training a model, you need to configure the learning process, which is done via the compile
method. It receives three arguments:
- An optimizer. This could be the string identifier of an existing optimizer (such as
rmsprop
oradagrad
), or an instance of theOptimizer
class. See: optimizers. - A loss function. This is the objective that the model will try to minimize. It can be the string identifier of an existing loss function (such as
categorical_crossentropy
ormse
), or it can be an objective function. See: losses. - A list of metrics. For any classification problem you will want to set this to
metrics=['accuracy']
. A metric could be the string identifier of an existing metric or a custom metric function.
# For a multi-class classification problemmodel.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])# For a binary classification problemmodel.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])# For a mean squared error regression problemmodel.compile(optimizer='rmsprop', loss='mse')# For custom metricsimport keras.backend as Kdef mean_pred(y_true, y_pred): return K.mean(y_pred)model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy', mean_pred])
Training
Keras models are trained on Numpy arrays of input data and labels. For training a model, you will typically use the fit
function. Read its documentation here.
# For a single-input model with 2 classes (binary classification):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 dataimport numpy as npdata = np.random.random((1000, 100))labels = np.random.randint(2, size=(1000, 1))# Train the model, iterating on the data in batches of 32 samplesmodel.fit(data, labels, epochs=10, batch_size=32)
# For a single-input model with 10 classes (categorical classification):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 dataimport numpy as npdata = np.random.random((1000, 100))labels = np.random.randint(10, size=(1000, 1))# Convert labels to categorical one-hot encodingone_hot_labels = keras.utils.to_categorical(labels, num_classes=10)# Train the model, iterating on the data in batches of 32 samplesmodel.fit(data, one_hot_labels, epochs=10, batch_size=32)
Examples
Here are a few examples to get you started!
In the examples folder, you will also find example models for real datasets:
- CIFAR10 small images classification: Convolutional Neural Network (CNN) with realtime data augmentation
- IMDB movie review sentiment classification: LSTM over sequences of words
- Reuters newswires topic classification: Multilayer Perceptron (MLP)
- MNIST handwritten digits classification: MLP & CNN
- Character-level text generation with LSTM
…and more.
Multilayer Perceptron (MLP) for multi-class softmax classification:
from keras.models import Sequentialfrom keras.layers import Dense, Dropout, Activationfrom keras.optimizers import SGD# Generate dummy dataimport numpy as npx_train = np.random.random((1000, 20))y_train = keras.utils.to_categorical(np.random.randint(10, size=(1000, 1)), num_classes=10)x_test = np.random.random((100, 20))y_test = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)model = Sequential()# Dense(64) is a fully-connected layer with 64 hidden units.# in the first layer, you must specify the expected input data shape:# here, 20-dimensional vectors.model.add(Dense(64, activation='relu', input_dim=20))model.add(Dropout(0.5))model.add(Dense(64, 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, metrics=['accuracy'])model.fit(x_train, y_train, epochs=20, batch_size=128)score = model.evaluate(x_test, y_test, batch_size=128)
MLP for binary classification:
import numpy as npfrom keras.models import Sequentialfrom keras.layers import Dense, Dropout# Generate dummy datax_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-like convnet:
import numpy as npimport kerasfrom keras.models import Sequentialfrom keras.layers import Dense, Dropout, Flattenfrom keras.layers import Conv2D, MaxPooling2Dfrom keras.optimizers import SGD# Generate dummy datax_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)
Sequence classification with LSTM:
from keras.models import Sequentialfrom keras.layers import Dense, Dropoutfrom keras.layers import Embeddingfrom keras.layers import LSTMmodel = 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)
Sequence classification with 1D convolutions:
from keras.models import Sequentialfrom keras.layers import Dense, Dropoutfrom keras.layers import Embeddingfrom keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1Dmodel = 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)
Stacked LSTM for sequence classification
In this model, we stack 3 LSTM layers on top of each other,
making the model capable of learning higher-level temporal representations.
The first two LSTMs return their full output sequences, but the last one only returns
the last step in its output sequence, thus dropping the temporal dimension
(i.e. converting the input sequence into a single vector).
from keras.models import Sequentialfrom keras.layers import LSTM, Denseimport numpy as npdata_dim = 16timesteps = 8num_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 32model.add(LSTM(32, return_sequences=True)) # returns a sequence of vectors of dimension 32model.add(LSTM(32)) # return a single vector of dimension 32model.add(Dense(10, activation='softmax'))model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])# Generate dummy training datax_train = np.random.random((1000, timesteps, data_dim))y_train = np.random.random((1000, num_classes))# Generate dummy validation datax_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))
Same stacked LSTM model, rendered “stateful”
A stateful recurrent model is one for which the internal states (memories) obtained after processing a batch
of samples are reused as initial states for the samples of the next batch. This allows to process longer sequences
while keeping computational complexity manageable.
You can read more about stateful RNNs in the FAQ.
from keras.models import Sequentialfrom keras.layers import LSTM, Denseimport numpy as npdata_dim = 16timesteps = 8num_classes = 10batch_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 datax_train = np.random.random((batch_size * 10, timesteps, data_dim))y_train = np.random.random((batch_size * 10, num_classes))# Generate dummy validation datax_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))
- 开始 Keras 序列模型(Sequential model)
- keras中文-快速开始Sequential模型
- Keras-Sequential模型(1)
- Keras-Sequential模型(2)
- keras系列︱Sequential与Model模型、keras基本结构功能(一)
- keras系列︱Sequential与Model模型、keras基本结构功能(一)
- Keras 序贯(sequential)模型简介
- keras学习笔记-二(Sequential 模型)
- keras系列︱Application中五款已训练模型、VGG16框架(Sequential式、Model式)解读(二)
- keras系列︱Application中五款已训练模型、VGG16框架(Sequential式、Model式)解读(二)
- keras学习随笔02——Sequential模型
- 11.11学习笔记之keras的sequential模型
- 2——快速开始Sequential模型
- keras_序惯模型(Sequential)
- Keras笔记(一)关于Keras模型
- Sequential Model-Based Optimization(SMBO)
- Sequential模型接口
- Keras模型
- Linux上的"Hello world"
- 浏览器的同源策略
- uva 10325 The Lottery 枚举子集+容斥原理
- 大话设计模式读后感之原型模式
- Being a Good Boy in Spring Festival (hdu1850)
- 开始 Keras 序列模型(Sequential model)
- 键盘上下键控制下拉列表上下切换
- 怎样永久关闭Win10自动更新_win10官网
- 测试员,敢问路在何方?来自微软工程师
- 欢迎使用CSDN-markdown编辑器
- 例题1.4 Graveyard UVALive
- mysql --- join ,where
- printf 的宏定义版本
- scrapy提示DEBUG:Filtered offsite request to