def __init__(self, feature_map, model_id='CCPM', task='binary_classification', learning_rate=0.001, embedding_initializer='torch.nn.init.normal_(std=1e-4)', embedding_dim=10, channels=[4, 4, 2], kernel_heights=[6, 5, 3], activation='Tanh', **kwargs):
    super(CCPM, self).__init__(feature_map, model_id=model_id, **kwargs)
    self.embedding_layer = EmbeddingLayer(feature_map, embedding_dim)
    self.conv_layer = CCPM_ConvLayer(feature_map.num_fields, channels=channels, kernel_heights=kernel_heights, activation=activation)
    conv_out_dim = ((3 * embedding_dim) * channels[(- 1)])
    self.fc = nn.Linear(conv_out_dim, 1)
    self.output_activation = self.get_final_activation(task)
    self.init_weights(embedding_initializer=embedding_initializer)
def __init__(self, feature_map, model_id='CCPM', task='binary_classification', learning_rate=0.001, embedding_initializer='torch.nn.init.normal_(std=1e-4)', embedding_dim=10, channels=[4, 4, 2], kernel_heights=[6, 5, 3], activation='Tanh', **kwargs):
    super(CCPM, self).__init__(feature_map, model_id=model_id, **kwargs)
    self.embedding_layer = EmbeddingLayer(feature_map, embedding_dim)
    self.conv_layer = CCPM_ConvLayer(feature_map.num_fields, channels=channels, kernel_heights=kernel_heights, activation=activation)
    conv_out_dim = ((3 * embedding_dim) * channels[(- 1)])
    self.fc = nn.Linear(conv_out_dim, 1)
    self.output_activation = self.get_final_activation(task)
    self.init_weights(embedding_initializer=embedding_initializer)