keras模型（分类）

from keras.models import Sequential#序贯模型
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD#优化器
from keras import metrics#参数评估模块
import keras
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)#ont_hot编码
x_test = np.random.random((100, 20))
y_test = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)
#构建模型
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=[metrics.categorical_accuracy, metrics.mae])
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])
#训练模型
model.fit(x_train, y_train,
          epochs=20,
          batch_size=128)
#评估模型
score = model.evaluate(x_train, y_train, batch_size=128)
print(score)

这个是多分类，下面的是二分类

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)

！！！！！敲重点！！！！！！！！

二分类与多分类的去区别

二分类不用编码，而多分类需要编码，分类的数量跟你输入的数据的维度有关。

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二分类的Ground turth 也是one_hot编码

keras模型（分类）

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