Upload deeprx_b3.py

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)