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TF使用Keras

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APIS

compile参数介绍

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model.compile(
   optimizer, 
   loss = None, 
   metrics = None, 
   loss_weights = None, 
   sample_weight_mode = None, 
   weighted_metrics = None, 
   target_tensors = None
)

optimizer：优化器，用于控制梯度裁剪。必选项
loss：损失函数（或称目标函数、优化评分函数）。必选项
metrics：评价函数用于评估当前训练模型的性能。当模型编译后（compile），评价函数应该作为 metrics 的参数来输入。评价函数和损失函数相似，只不过评价函数的结果不会用于训练过程中。

https://blog.csdn.net/huang1024rui/article/details/120055487

TF2.3-Keras Sequential 顺序模型

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from keras.models import Sequential
from keras.layers import Dense, Activation

model = Sequential([
    Dense(32, input_shape=(784,)),
    Activation('relu'),
    Dense(10),
    Activation('softmax'),
])
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model = Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))

输入数据

等价片段

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model = Sequential()
model.add(Dense(32, input_shape=(784,)))

model = Sequential()
model.add(Dense(32, input_dim=784))

编译

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# 多分类问题
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# 二分类问题
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# 均方误差回归问题
model.compile(optimizer='rmsprop',
              loss='mse')

# 自定义评估标准函数
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])

Train

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# 对于具有 2 个类的单输入模型（二进制分类）：

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'])

# 生成虚拟数据
import numpy as np
data = np.random.random((1000, 100))
labels = np.random.randint(2, size=(1000, 1))

# 训练模型，以 32 个样本为一个 batch 进行迭代
model.fit(data, labels, epochs=10, batch_size=32)
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# 对于具有 10 个类的单输入模型（多分类分类）：

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'])

# 生成虚拟数据
import numpy as np
data = np.random.random((1000, 100))
labels = np.random.randint(10, size=(1000, 1))

# 将标签转换为分类的 one-hot 编码
one_hot_labels = keras.utils.to_categorical(labels, num_classes=10)

# 训练模型，以 32 个样本为一个 batch 进行迭代
model.fit(data, one_hot_labels, epochs=10, batch_size=32)

Demo

基于多层感知器 (MLP) 的 softmax 多分类：

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import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD

# 生成虚拟数据
import numpy as np
x_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) 是一个具有 64 个隐藏神经元的全连接层。
# 在第一层必须指定所期望的输入数据尺寸：
# 在这里，是一个 20 维的向量。
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)

二分类

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import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout

# 生成虚拟数据
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CNN

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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

# 生成虚拟数据
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()
# 输入: 3 通道 100x100 像素图像 -> (100, 100, 3) 张量。
# 使用 32 个大小为 3x3 的卷积滤波器。
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

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from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Embedding
from keras.layers import LSTM

max_features = 1024

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)

1D卷积序列分类

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from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D

seq_length = 64

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)

基于栈式 LSTM 的序列分类

在这个模型中，我们将 3 个 LSTM 层叠在一起，使模型能够学习更高层次的时间表示。

前两个 LSTM 返回完整的输出序列，但最后一个只返回输出序列的最后一步，从而降低了时间维度（即将输入序列转换成单个向量）。

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from keras.models import Sequential
from keras.layers import LSTM, Dense
import numpy as np

data_dim = 16
timesteps = 8
num_classes = 10

# 期望输入数据尺寸: (batch_size, timesteps, data_dim)
model = Sequential()
model.add(LSTM(32, return_sequences=True,
               input_shape=(timesteps, data_dim)))  # 返回维度为 32 的向量序列
model.add(LSTM(32, return_sequences=True))  # 返回维度为 32 的向量序列
model.add(LSTM(32))  # 返回维度为 32 的单个向量
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

# 生成虚拟训练数据
x_train = np.random.random((1000, timesteps, data_dim))
y_train = np.random.random((1000, num_classes))

# 生成虚拟验证数据
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))

“stateful” 渲染的的栈式 LSTM 模型

有状态 (stateful) 的循环神经网络模型中，在一个 batch 的样本处理完成后，其内部状态（记忆）会被记录并作为下一个 batch 的样本的初始状态。这允许处理更长的序列，同时保持计算复杂度的可控性。

你可以在 FAQ 中查找更多关于 stateful RNNs 的信息。

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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

# 期望输入数据尺寸: (batch_size, timesteps, data_dim)
# 请注意，我们必须提供完整的 batch_input_shape，因为网络是有状态的。
# 第 k 批数据的第 i 个样本是第 k-1 批数据的第 i 个样本的后续。
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'])

# 生成虚拟训练数据
x_train = np.random.random((batch_size * 10, timesteps, data_dim))
y_train = np.random.random((batch_size * 10, num_classes))

# 生成虚拟验证数据
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))

参考资料

Keras 学习笔记（三）Keras Sequential 顺序模型 - 腾讯云开发者社区-腾讯云