def _build(self): # TODO Featureless self.layers.append( Dense(input_dim=self.dims[0], output_dim=self.dims[1], nnz_features=self.data['nnz_features'], dropout=self.dropouts[0], act=self.act[0], bias=self.bias, sparse_inputs=self.sparse_inputs[0], logging=self.logging, model_name=self.name)) for i in range(1, self.n_layers - 1): self.layers.append( self.conv_layer( layer_id=i, x_names=self.feature_names, dims=self.dims, bias=self.bias, weights=True, dropout=self.dropouts[i], # dropout=0., act=self.act[i], shared_weights=self.shared_weights, nnz_features=self.data['nnz_features'], sparse_inputs=self.sparse_inputs[i], skip_connection=self.skip_conn, add_labels=self.add_labels, logging=self.logging, model_name=self.name)) self.layers.append( Fusion(n_layers=self.n_layers - 2, x_names=self.feature_names, input_dim=self.dims[1], output_dim=self.dims[1], dropout=self.dropouts[i + 1], act=tf.nn.relu, bias=self.bias, logging=self.logging, model_name=self.name)) self.layers.append( Dense(input_dim=self.dims[-2], output_dim=self.output_dims, nnz_features=self.data['nnz_features'], dropout=self.drop_label, act=lambda x: x, bias=self.bias, sparse_inputs=self.sparse_inputs[-1], logging=self.logging, model_name=self.name))
def _build(self): # TODO Featureless for i in range(self.n_layers): self.layers.append( self.conv_layer(layer_id=i, x_names=self.feature_names, dims=self.dims, bias=self.bias, weights=True, dropout=self.dropouts[i], shared_weights=self.shared_weights, nnz_features=self.data['nnz_features'], sparse_inputs=self.sparse_inputs[i], skip_connection=self.skip_conn, add_labels=self.add_labels, logging=self.logging, model_name=self.name)) self.layers.append( Dense(input_dim=self.dims[-2], output_dim=self.dims[-1], nnz_features=None, dropout=self.dropouts[-1], act=self.act[-1], bias=self.bias, sparse_inputs=self.sparse_inputs[-1], logging=self.logging))
def main(): input_size = output_size = 2048 now = datetime.datetime.now() time = now.strftime('%Y.%m.%d %H.%M') inputs = Input((input_size, )) layer = Dense(5, activation='relu')(inputs) layer = Dropout(0.25)(layer) outputs = Dense(output_size, activation='relu')(layer) train_x, train_y = make_data(80000, output_size) test_x, test_y = make_data(20000, output_size) tensorboard = TensorBoard('./logs/' + time) model = Model(inputs, outputs) model.compile('adam', 'mean_squared_error') model.fit(train_x, train_y, epochs=20, validation_data=(test_x, test_y), callbacks=[tensorboard])
def _build(self): # TODO Featureless self.layers.append( Dense(input_dim=self.dims[0], output_dim=self.dims[1], nnz_features=self.data['nnz_features'], dropout=self.dropouts[0], act=self.act[0], bias=self.bias, sparse_inputs=self.sparse_inputs[0], logging=self.logging, model_name=self.name)) for i in range(1, self.n_layers): self.layers.append( Kernel(layer_id=i, x_names=self.feature_names, dims=self.dims, dropout=self.dropouts[i], act=self.act[i], bias=self.bias, shared_weights=self.shared_weights, nnz_features=self.data['nnz_features'], sparse_inputs=self.sparse_inputs[i], skip_connection=self.skip_conn, add_labels=self.add_labels, logging=self.logging, model_name=self.name)) self.layers.append( Fusion(n_layers=self.n_layers - 1, x_names=self.feature_names, input_dim=self.dims[1], output_dim=self.output_dims, dropout=self.drop_fuse, act=(lambda x: x), bias=self.bias, logging=self.logging, model_name=self.name))