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deeprx_zipped / deeprx_b3.py

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	#3 resnet block version
	import tensorflow as tf
	import scipy.io as sio
	import numpy as np
	from sklearn.model_selection import train_test_split
	from tensorflow_addons.optimizers import AdamW #pip install tensorflow_addons (Need to match tf version)

	def residual_block(x, filters, dilation_rate,kernel_size=3):
	# Shortcut分支
	shortcut = x

	# 第一个卷积层
	x = tf.keras.layers.BatchNormalization()(x)
	x = tf.keras.layers.ReLU()(x)
	x = tf.keras.layers.SeparableConv2D(filters, kernel_size, depth_multiplier=2,dilation_rate=dilation_rate, padding='same')(x)
	x = tf.keras.layers.Conv2D(filters, 1, padding='same')(x)
	x = tf.keras.layers.BatchNormalization()(x)

	# 第二个卷积层
	x = tf.keras.layers.ReLU()(x)
	x = tf.keras.layers.SeparableConv2D(filters, kernel_size,depth_multiplier=2, dilation_rate=dilation_rate, padding='same')(x)
	x = tf.keras.layers.Conv2D(filters, 1, padding='same')(x)

	# 如果维度不匹配，对shortcut进行适当的变换
	if shortcut.shape[-1] != filters:
	shortcut = tf.keras.layers.Conv2D(filters, 1, padding='same')(shortcut)

	# 相加操作
	x = tf.keras.layers.Add()([x, shortcut])
	x = tf.keras.layers.ReLU()(x)

	return x

	def build_resnet(input_shape):
	inputs = tf.keras.layers.Input(shape=input_shape)

	# 初始卷积层
	x = tf.keras.layers.Conv2D(64,3, strides=1, padding='same')(inputs)
	x = tf.keras.layers.BatchNormalization()(x)
	x = tf.keras.layers.ReLU()(x)

	# 堆叠ResNet块
	x = residual_block(x, filters=128, dilation_rate=(2,3))
	x = residual_block(x, filters=448, dilation_rate=(3,6))
	x = residual_block(x, filters=128, dilation_rate=(2,3))

	outputs = tf.keras.layers.Conv2D(Morder, 3, strides=1, padding='same')(x)
	outputs = tf.keras.activations.sigmoid(outputs)

	model = tf.keras.Model(inputs, outputs)
	return model

	def load_data(m):
	data_inputs = []
	data_labels = []
	for n in range(1,m+1):
	input_data = sio.loadmat(f"SNR{n}_input.mat")["input_save"]
	label_data = sio.loadmat(f"SNR{n}_label.mat")["label_save"]
	input_data = np.transpose(input_data, (3,0,1,2))
	label_data = np.transpose(label_data, (3,0,1,2))
	data_inputs.append(input_data)
	data_labels.append(label_data)
	data_inputs = np.concatenate(data_inputs)
	data_labels = np.concatenate(data_labels)
	return data_inputs, data_labels
	# 定义输入形状和类别数量
	start = time.time()
	input_shape = (312, 14, 6)
	Morder = 4 # 16QAM
	SNR_number = 10

	# 创建ResNet模型
	resnet_model = build_resnet(input_shape)
	# 打印模型概要
	resnet_model.summary()
	##################################################################

	# 定义AdamW优化器，并设置学习率为0.01
	#optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
	optimizer = AdamW(learning_rate=0.01, weight_decay=1e-4)
	# tensorboard
	log_dir = "./log"
	tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir =log_dir)
	# 编译模型
	resnet_model.compile(optimizer=optimizer, loss='binary_crossentropy',callbacks=[tensorboard_callback])
	# read data
	X_data,y_data = load_data(SNR_number)
	print(X_data.shape,y_data.shape)
	X_train, X_val, y_train, y_val = train_test_split(X_data, y_data, test_size=0.3, random_state=42)
	# 训练模型
	resnet_model.fit(X_train, y_train, epochs=10, batch_size=20, validation_data=(X_val, y_val))
	# 保存
	resnet_model.save("deeprx.h5")
	endt = time.time()
	print(endt-start)