drugai.py 文件源码

def Discriminator(y_dash, dropout=0.4, lr=0.00001, PATH="Dis.h5"):
    """Creates a discriminator model that takes an image as input and outputs a single value, representing whether
the input is real or generated. Unlike normal GANs, the output is not sigmoid and does not represent a probability!
Instead, the output should be as large and negative as possible for generated inputs and as large and positive
as possible for real inputs."""
    model = Sequential()
    model.add(Conv1D(input_shape=(y_dash.shape[1], y_dash.shape[2]),
                     nb_filter=25,
                     filter_length=4,
                     border_mode='same'))
    model.add(LeakyReLU())
    model.add(Dropout(dropout))
    model.add(MaxPooling1D())
    model.add(Conv1D(nb_filter=10,
                     filter_length=4,
                     border_mode='same'))
    model.add(LeakyReLU())
    model.add(Dropout(dropout))
    model.add(MaxPooling1D())
    model.add(Flatten())
    model.add(Dense(64))
    model.add(LeakyReLU())
    model.add(Dropout(dropout))
    model.add(Dense(1))
    model.add(Activation('linear'))

    opt = Adam(lr, beta_1=0.5, beta_2=0.9)

    #reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.9, patience=30, min_lr=0.000001, verbose=1)
    checkpoint_D = ModelCheckpoint(
        filepath=PATH, verbose=1, save_best_only=True)

    model.compile(optimizer=opt,
                  loss=wasserstein_loss,
                  metrics=['accuracy'])
    return model, checkpoint_D