7d6fcb1cfbf368a8c255b0dbab4b1f0ec4d2a48c,plasma/models/builder.py,ModelBuilder,build_model,#ModelBuilder#Any#Any#,73

Before Change

        //x_in = TimeDistributed(Dense(2*(num_0D+num_1D)),activation="relu") (x_in)
        // x = TimeDistributed(Dense(2*(num_0D+num_1D)))
 //               model.add(TimeDistributed(Dense(num_density_channels,bias=True),batch_input_shape=batch_input_shape))
        for _ in range(model_conf["rnn_layers"]):
            x_in = rnn_model(rnn_size, return_sequences=return_sequences,//batch_input_shape=batch_input_shape,
             stateful=stateful,kernel_regularizer=l2(regularization),recurrent_regularizer=l2(regularization),
             bias_regularizer=l2(regularization),dropout=dropout_prob,recurrent_dropout=dropout_prob) (x_in)
            x_in = Dropout(dropout_prob) (x_in)
        if return_sequences:
            //x_out = TimeDistributed(Dense(100,activation="tanh")) (x_in)
            x_out = TimeDistributed(Dense(1,activation=output_activation)) (x_in)
        else:

After Change

                pre_rnn_1D = MaxPooling1D(pool_size) (pre_rnn_1D)
            pre_rnn_1D = Flatten() (pre_rnn_1D)
            pre_rnn_1D = Dense(dense_size,kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn_1D)
            if use_batch_norm: pre_rnn_1D = BatchNormalization()(pre_rnn_1D)
            pre_rnn_1D = Activation("relu")(pre_rnn_1D)
            pre_rnn_1D = Dense(dense_size//4,kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn_1D)
            if use_batch_norm: pre_rnn_1D = BatchNormalization()(pre_rnn_1D)
            pre_rnn_1D = Activation("relu")(pre_rnn_1D)
            pre_rnn = Concatenate() ([pre_rnn_0D,pre_rnn_1D])
        else:
            pre_rnn = pre_rnn_input        

        if model_conf["rnn_layers"] == 0 or model_conf["extra_dense_input"]:
            pre_rnn = Dense(dense_size,activation="relu",kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn)
            pre_rnn = Dense(dense_size//2,activation="relu",kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn)
            pre_rnn = Dense(dense_size//4,activation="relu",kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn)
        
        pre_rnn_model = Model(inputs = pre_rnn_input,outputs=pre_rnn)
        pre_rnn_model.summary()
        x_input = Input(batch_shape = batch_input_shape)
        x_in = TimeDistributed(pre_rnn_model) (x_input)

        if use_bidirectional:

Instances