Question

我正在尝试将6类时频域信号（STFT频谱图）分类为3601x217像素。假设每个课程有70个训练样本，20个验证样本和10个测试样本。每个样本都是尺寸为3601x217x1（灰度）的PNG图像，而将来我正在考虑将其扩展为3601x217x4（3个颜色通道+ alpha）。但是现在，我首先关注灰度。

尝试纯LSTM后，我发现该模型很快过拟合（训练精度> 90％，但是val_acc停留在20％）。 ow，我想尝试一下Time Distributed CNN + LSTM。

以下是代码：

img_width, img_height = 3601,217
train_data_dir = 'sensor1/training'
validation_data_dir = 'sensor1/validation'

num_classes = 6
nb_train_samples = 70
nb_validation_samples = 20
epochs = 50
batch_size = 5

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

model = Sequential()
model.add(TimeDistributed(Conv2D(16, (3,3), padding='same', strides=(2,2),             activation='relu', input_shape = input_shape)))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))
model.add(Dropout(0.5))
model.add(TimeDistributed(Conv2D(32, (3,3), padding='same', strides=(2,2),     activation='relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))
model.add(Dropout(0.5))
model.add(TimeDistributed(Conv2D(64, (3,3), padding='same', strides=(2,2), activation='relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))
model.add(Dropout(0.5))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(units=128, return_sequences=False))
model.add(LSTM(units=64, return_sequences=False))
model.add(Dense(32))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

train_datagen = ImageDataGenerator(rescale = 1. / 255)
test_datagen = ImageDataGenerator(rescale = 1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir, target_size=(img_width, img_height),
    batch_size=batch_size, color_mode='grayscale')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir, target_size=(img_width, img_height),
    batch_size=batch_size, color_mode='grayscale')

model.fit_generator(
    train_generator, steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs, callbacks=[plot_losses],
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

代码给出以下错误：

_make_train_function中的文件“ C：\ ProgramData \ Anaconda3 \ lib \ site-packages \ keras \ engine \ training.py”，第496行，引发RuntimeError（“使用模型之前必须先对其进行编译。”） RuntimeError：您必须先编译模型，然后再使用它。

代码有什么问题？我实际上是在调用model.fit_generator之前编译了模型：

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

如何为CNN + LSTM使用TimeDistributed？

0 个答案: