卷積神經網路實現多個數字識別
阿新 • • 發佈:2019-01-09
from keras.models import Modelfrom keras.layers import *import tensorflow as tf# This returns a tensorinputs = Input(shape=(28, 140, 1))
conv_11 = Conv2D(filters= 32, kernel_size=(5,5), padding='Same', activation='relu')(inputs)
max_pool_11 = MaxPool2D(pool_size=(2,2))(conv_11)
conv_12 = Conv2D(filters= 10, kernel_size=(3,3), padding='Same', activation='relu')(max_pool_11)
max_pool_12 = MaxPool2D(pool_size=(2,2), strides=(2,2))(conv_12)
flatten11 = Flatten()(max_pool_12)
hidden11 = Dense(15, activation='relu')(flatten11)
prediction1 = Dense(11, activation='softmax')(hidden11)
hidden21 = Dense(15, activation='relu')(flatten11)
prediction2 = Dense(11, activation='softmax')(hidden21)
hidden31 = Dense(15, activation='relu')(flatten11)
prediction3 = Dense(11, activation='softmax')(hidden31)
hidden41 = Dense(15, activation='relu')(flatten11)
prediction4 = Dense(11, activation='softmax')(hidden41)
hidden51 = Dense(15, activation='relu')(flatten11)
prediction5 = Dense(11, activation='softmax')(hidden51)
model = Model(inputs=inputs, outputs=[prediction1,prediction2,prediction3,prediction4,prediction5])
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
conv_11 = Conv2D(filters= 32, kernel_size=(5,5), padding='Same', activation='relu')(inputs)
max_pool_11 = MaxPool2D(pool_size=(2,2))(conv_11)
conv_12 = Conv2D(filters= 10, kernel_size=(3,3), padding='Same', activation='relu')(max_pool_11)
max_pool_12 = MaxPool2D(pool_size=(2,2), strides=(2,2))(conv_12)
flatten11 = Flatten()(max_pool_12)
hidden11 = Dense(15, activation='relu')(flatten11)
prediction1 = Dense(11, activation='softmax')(hidden11)
hidden21 = Dense(15, activation='relu')(flatten11)
prediction2 = Dense(11, activation='softmax')(hidden21)
hidden31 = Dense(15, activation='relu')(flatten11)
prediction3 = Dense(11, activation='softmax')(hidden31)
hidden41 = Dense(15, activation='relu')(flatten11)
prediction4 = Dense(11, activation='softmax')(hidden41)
hidden51 = Dense(15, activation='relu')(flatten11)
prediction5 = Dense(11, activation='softmax')(hidden51)
model = Model(inputs=inputs, outputs=[prediction1,prediction2,prediction3,prediction4,prediction5])
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',