classification_mlp.py 文件源码

 复制代码def train_mlp1(x_train, y_train, x_test, y_test, input_dim, num_classes=24):
    """

    :param x_train:
    :param y_train:
    :param x_test:
    :param y_test:
    :param input_dim:
    :param num_classes:
    :return:
    """
    model = Sequential()
    model.add(Dense(512, input_dim=input_dim))
    model.add(Activation('relu'))   # An "activation" is just a non-linear function applied to the output of the layer
                                    # above. Here, with a "rectified linear unit", we clamp all values below 0 to 0.
    model.add(Dropout(0.1))        # Dropout helps protect the model from memorizing or "overfitting" the training data
    model.add(Dense(1024))
    model.add(Activation('relu'))

    model.add(Dropout(0.1))
    model.add(Dense(386))
    model.add(Activation('relu'))

    model.add(Dropout(0.1))
    model.add(Dense(num_classes))
    model.add(Activation256('softmax'))  # This special "softmax" activation among other things,
                                      # ensures the output is a valid probability distribution, that is
                                      # that its values are all non-negative and sum to 1.

    model.compile(loss='categorical_crossentropy', optimizer=Adamax(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=1e-5), metrics=["accuracy"])
    model.fit(x_train, y_train,
              batch_size=40, nb_epoch=16, verbose=1,
              validation_data=(x_test, y_test))
    score = model.evaluate(x_test, y_test, verbose=1)
    return score[1]