Row 6932

Row ID: 6932 | Dataset Entry | Axioma AXP Content Repository

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Hello everyone, I'm working on a deep learning project for detecting specific features in signals using TensorFlow. Below is the overview of my approach and where I'm facing issues.

## Data Description - \*\*Data Type\*\*: Complex-valued signal data. - \*\*Size and Structure\*\*: 310,000 signal, each with 2048 data points.(60% training,20%test,20%validation)

I've attached plots of training and validation accuracy and loss here:

https://preview.redd.it/1l8z5ucy5d0d1.png?width=1010&format=png&auto=webp&s=5d18a4dcba7b1a9c0665c0521d0f7af5f15cd365

## Model Architecture:

def residual_block(x, filters, kernel_size=3, stride=1, increase_filter=False): """ Defines a residual block with an optional convolution in the shortcut path to adjust dimensions. """ # Shortcut shortcut = x if increase_filter or stride > 1: shortcut = Conv1D(filters, 1, strides=stride, padding='same')(shortcut) shortcut = BatchNormalization()(shortcut) x = Conv1D(filters, kernel_size, strides=stride, padding='same')(x) x = BatchNormalization()(x) x = ReLU()(x) x = Conv1D(filters, kernel_size, padding='same')(x) x = BatchNormalization()(x) x = Add()([x, shortcut]) x = ReLU()(x) return x def build_cnn_model(input_shape): inputs = Input(shape=input_shape) x = Conv1D(32, 3, strides=2, padding='same')(inputs) x = BatchNormalization()(x) x = ReLU()(x) x = MaxPooling1D(3, strides=2, padding='same')(x) x = residual_block(x, 32) x = BatchNormalization()(x) x = residual_block(x, 32) x = BatchNormalization()(x) x = residual_block(x, 64, stride=2, increase_filter=True) x = BatchNormalization()(x) x = residual_block(x, 64) x = BatchNormalization()(x) x = residual_block(x, 128, stride=2, increase_filter=True) x = BatchNormalization()(x) x = residual_block(x, 128) x = BatchNormalization()(x) x = residual_block(x, 256, stride=2, increase_filter=True) x = BatchNormalization()(x) x = residual_block(x, 256) x = GlobalAveragePooling1D()(x) x = Dropout(0.2)(x) x = Dense(256, activation='relu')(x) outputs = Dense(2, activation='softmax')(x) model = Model(inputs=inputs, outputs=outputs) #optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, decay=0.0, amsgrad=False) model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy']) return model Thank you for any suggestions or insights you can provide!

FieldValue
text Hello everyone, I'm working on a deep learning project for detecting specific features in signals using TensorFlow. Below is the overview of my approach and where I'm facing issues. ## Data Description - \*\*Data Type\*\*: Complex-valued signal data. - \*\*Size and Structure\*\*: 310,000 signal, each with 2048 data points.(60% training,20%test,20%validation) I've attached plots of training and validation accuracy and loss here: https://preview.redd.it/1l8z5ucy5d0d1.png?width=1010&…
label r/deeplearning
dataType post
communityName r/deeplearning
datetime 2024-05-14
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url_encoded Z0FBQUFBQm5Lak9HNmtIRHpFRDlHQ2JpZkFpY0xVUTh1Q3J0YklmbEJLT0JocENBbEZOS1ZSM0l6UXprZXNuNTd1dzhYdThuemVUVWxqSjNYOHVfN2lwWDRoeW1PeDVhc3ZBLUxiWTR4bFNYU3h6VGlraFprS19SRF9leW1XcFpXLTBYWmpPVEgtSG13bE9UNndWYnRUSTVrM1l2MW5LQWhtRzhHM2RNTFZFLWNEdkE0VjBZb0xSWFlZQkpQcG9qT1RYdVBkb0VheDVIOFFra09IRkF6ei10dGZvRDVWRnJXdz09

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  "text": "Hello everyone,  \n  \nI'm working on a deep learning project for detecting specific features in signals using TensorFlow. Below is the overview of my approach and where I'm facing issues.\n\n  \n## Data Description  \n- \\*\\*Data Type\\*\\*: Complex-valued signal data.  \n- \\*\\*Size and Structure\\*\\*: 310,000 signal, each with 2048 data points.(60% training,20%test,20%validation)\n\nI've attached plots of training and validation accuracy and loss here:\n\nhttps://preview.redd.it/1l8z5ucy5d0d1.png?width=1010&format=png&auto=webp&s=5d18a4dcba7b1a9c0665c0521d0f7af5f15cd365\n\n## Model Architecture:\n\n    def residual_block(x, filters, kernel_size=3, stride=1, increase_filter=False):\n        \"\"\" Defines a residual block with an optional convolution in the shortcut path to adjust dimensions. \"\"\"\n        # Shortcut\n        shortcut = x\n        if increase_filter or stride > 1:\n            shortcut = Conv1D(filters, 1, strides=stride, padding='same')(shortcut)\n            shortcut = BatchNormalization()(shortcut)\n        x = Conv1D(filters, kernel_size, strides=stride, padding='same')(x)\n        x = BatchNormalization()(x)\n        x = ReLU()(x)\n        x = Conv1D(filters, kernel_size, padding='same')(x)\n        x = BatchNormalization()(x)\n        x = Add()([x, shortcut])\n        x = ReLU()(x)\n        return x\n    \n    def build_cnn_model(input_shape):\n        inputs = Input(shape=input_shape)\n        x = Conv1D(32, 3, strides=2, padding='same')(inputs)\n        x = BatchNormalization()(x)\n        x = ReLU()(x)\n        x = MaxPooling1D(3, strides=2, padding='same')(x)\n        x = residual_block(x, 32)\n        x = BatchNormalization()(x)\n        x = residual_block(x, 32)\n        x = BatchNormalization()(x)\n        x = residual_block(x, 64, stride=2, increase_filter=True)\n        x = BatchNormalization()(x)\n        x = residual_block(x, 64)\n        x = BatchNormalization()(x)\n        x = residual_block(x, 128, stride=2, increase_filter=True)\n        x = BatchNormalization()(x)\n        x = residual_block(x, 128)\n        x = BatchNormalization()(x)\n        x = residual_block(x, 256, stride=2, increase_filter=True)\n        x = BatchNormalization()(x)\n        x = residual_block(x, 256)\n        x = GlobalAveragePooling1D()(x)\n        x = Dropout(0.2)(x)\n        x = Dense(256, activation='relu')(x)\n        outputs = Dense(2, activation='softmax')(x)\n        model = Model(inputs=inputs, outputs=outputs)\n        #optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, decay=0.0, amsgrad=False)\n        model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])\n        return model\n    \n    Thank you for any suggestions or insights you can provide!",
  "label": "r/deeplearning",
  "dataType": "post",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-14",
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}
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