skopt.py 文件源码

def my_model(dropout):
    ############ model params ################
    line_length = 248  # seq size
    train_char = 58
    hidden_neurons = 512  # hidden neurons
    batch = 64  # batch_size
    no_epochs = 5
    ################### Model ################
    model = Sequential()
    # layer 1
    model.add(LSTM(hidden_neurons, return_sequences=True,
                   input_shape=(line_length, train_char)))
    model.add(Dropout(dropout))
    # layer 2
    model.add(LSTM(hidden_neurons, return_sequences=True))
    model.add(Dropout(dropout))
    # layer 3
    model.add(LSTM(hidden_neurons, return_sequences=True))
    model.add(Dropout(dropout))
    model.add(Reshape((248, 512)))
    # fc layer
    model.add(TimeDistributed(Dense(58, activation='softmax')))
    # model.load_weights("weights/model_maha1_noep50_batch64_seq_248.hdf5")
    # model.layers.pop()
    # model.layers.pop()
    # model.add(Dropout(dropout))
    #model.add(TimeDistributed(Dense(train_char, activation='softmax')))
    initlr = 0.00114
    adagrad = Adagrad(lr=initlr, epsilon=1e-08)
    model.compile(optimizer=adagrad,
                  loss='categorical_crossentropy', metrics=['accuracy'])
    ###load weights####
    return model