hypopt.py 文件源码

def my_model(X_train, y_train, X_test, y_test):
    ############ model params ################
    line_length = 248  # seq size
    train_char = 58
    hidden_neurons = 512  # hidden neurons
    batch = 64  # batch_size
    no_epochs = 3
    ################### Model ################

    ######### begin model ########
    model = Sequential()
    # layer 1
    model.add(LSTM(hidden_neurons, return_sequences=True,
                   input_shape=(line_length, train_char)))
    model.add(Dropout({{choice([0.4, 0.5, 0.6, 0.7, 0.8])}}))
    # layer 2
    model.add(LSTM(hidden_neurons, return_sequences=True))
    model.add(Dropout({{choice([0.4, 0.5, 0.6, 0.7, 0.8])}}))
    # layer 3
    model.add(LSTM(hidden_neurons, return_sequences=True))
    model.add(Dropout({{choice([0.4, 0.5, 0.6, 0.7, 0.8])}}))
    # fc layer
    model.add(TimeDistributed(Dense(train_char, activation='softmax')))
    model.load_weights("weights/model_maha1_noep50_batch64_seq_248.hdf5")
    ########################################################################
    checkpoint = ModelCheckpoint("weights/hypmodel2_maha1_noep{0}_batch{1}_seq_{2}.hdf5".format(
        no_epochs, batch, line_length), monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='min')

    initlr = 0.00114
    adagrad = Adagrad(lr=initlr, epsilon=1e-08,
                      clipvalue={{choice([0, 1, 2, 3, 4, 5, 6, 7])}})
    model.compile(optimizer=adagrad,
                  loss='categorical_crossentropy', metrics=['accuracy'])
    history = History()
    # fit model
    model.fit(X_train, y_train, batch_size=batch, nb_epoch=no_epochs,
              validation_split=0.2, callbacks=[history, checkpoint])

    score, acc = model.evaluate(X_test, y_test, verbose=0)
    print('Test accuracy:', acc)
    return {'loss': -acc, 'status': STATUS_OK, 'model': model}