代码拉取完成,页面将自动刷新
同步操作将从 Huoyo/ndraw 强制同步,此操作会覆盖自 Fork 仓库以来所做的任何修改,且无法恢复!!!
确定后同步将在后台操作,完成时将刷新页面,请耐心等待。
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@Desc :
@Author: Chang Zhang
@Date : 2021/12/1 20:31
'''
import tensorflow as tf
def model():
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(100, 5))
model.add(tf.keras.layers.Conv2D(10, 3))
model.add(tf.keras.layers.MaxPool2D())
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(200))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.Dense(20))
model.add(tf.keras.layers.Dense(2))
model.add(tf.keras.layers.Softmax())
model.build(input_shape=(None, 28, 28, 3))
return model
def model2():
x1 = tf.keras.layers.Input(shape=(None, 100))
x = tf.keras.layers.Dense(100)(x1)
# x = tf.keras.layers.Dense(40)(x)
x2 = tf.keras.layers.Input(shape=(None, 100))
x3 = tf.keras.layers.Input(shape=(None, 100))
x = tf.keras.layers.concatenate([x, x2, x3])
# x = tf.keras.layers.Dense(300)(x)
# x = tf.keras.layers.Dense(100)(x)
out = tf.keras.layers.Dense(2, activation="softmax")(x)
out2 = tf.keras.layers.Dense(2, activation="softmax")(x)
model = tf.keras.Model(inputs=[x1, x2, x3], outputs=[out,out2])
return model
def model3():
model = tf.keras.Sequential([
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(2, activation='softmax')
])
model.build(input_shape=(None, 100))
return model
def model4():
input = tf.keras.layers.Input(shape=(128, 192, 3))
x = tf.keras.layers.Permute((2, 1,3), input_shape=(128, 192, 3))(input)
x = tf.keras.layers.Conv2D(32, 1, padding='same', activation='relu')(x)
x = tf.keras.layers.MaxPooling2D()(x) #96x64
x = tf.keras.layers.Conv2D(64, 1, padding='same', activation='relu')(x)
x = tf.keras.layers.MaxPooling2D()(x) #48x32
x = tf.keras.layers.BatchNormalization()(x) #48x32
x = tf.keras.layers.Conv2D(128, 3, padding='same', activation='relu')(x)
x = tf.keras.layers.Conv2D(128, 3, padding='same', activation='relu')(x)
x = tf.keras.layers.MaxPooling2D()(x) #24x16
x = tf.keras.layers.BatchNormalization()(x) # 48x32
x = tf.keras.layers.Conv2D(128, 3, padding='same', activation='relu')(x)
x = tf.keras.layers.Conv2D(128, 3, padding='same', activation='relu')(x)
x = tf.keras.layers.MaxPooling2D()(x)#12x8
x = tf.keras.layers.BatchNormalization()(x) # 48x32
x = tf.keras.layers.Conv2D(256, 3, padding='same', activation='relu')(x)
x = tf.keras.layers.Conv2D(256, 3, padding='same', activation='relu')(x)
x = tf.keras.layers.MaxPooling2D()(x)#6x4
x = tf.keras.layers.BatchNormalization()(x) # 48x32
x = tf.keras.layers.Reshape((24,256))(x)
x = tf.keras.layers.LSTM(256)(x)
x = tf.keras.layers.RepeatVector(6)(x)
x = tf.keras.layers.LSTM(256,return_sequences=True)(x)
x = tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(37, activation='softmax'))(x)
model = tf.keras.Model(inputs=input, outputs=x)
return model
import ndraw
#该方式会在本地生成一个model.html的文件 直接浏览器打开即可
# ndraw.render(model2(),theme=ndraw.BLACK_WHITE)
# 该方式会启动一个web服务 本地9999端口访问
ndraw.server(model4(),theme=ndraw.Defualt,flow=ndraw.VERTICAL)
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