Question

我曾经使用keras 1（可能是1.0.5）进行多类别分类。我在CNN中的输入是（n，1,24,113），113是通道号，内核大小是（1,5）。代码如：

X_train = X_train.reshape((-1, 1, SLIDING_WINDOW_LENGTH, NUM_SENSOR_CHANNELS))
X_test = X_test.reshape((-1, 1, SLIDING_WINDOW_LENGTH, NUM_SENSOR_CHANNELS))

# network
inputs = Input(shape=(1, SLIDING_WINDOW_LENGTH, NUM_SENSOR_CHANNELS))
conv1 = ELU()(Convolution2D(NUM_FILTERS, FILTER_SIZE, 1, border_mode='valid', init='normal', activation='relu')(inputs))
conv2 = ELU()(Convolution2D(NUM_FILTERS, FILTER_SIZE, 1, border_mode='valid', init='normal', activation='relu')(conv1))
conv3 = ELU()(Convolution2D(NUM_FILTERS, FILTER_SIZE, 1, border_mode='valid', init='normal', activation='relu')(conv2))
conv4 = ELU()(Convolution2D(NUM_FILTERS, FILTER_SIZE, 1, border_mode='valid', init='normal', activation='relu')(conv3))
reshape1 = Reshape((8, NUM_FILTERS * NUM_SENSOR_CHANNELS))(conv4)
gru1 = GRU(NUM_UNITS_LSTM, return_sequences=True, consume_less='mem')(reshape1)
gru2 = GRU(NUM_UNITS_LSTM, return_sequences=False, consume_less='mem')(gru1)
outputs = Dense(NUM_CLASSES, activation='softmax')(gru2)

# Hardcoded number of sensor channels employed in the OPPORTUNITY challenge
NUM_SENSOR_CHANNELS = 113

# Hardcoded number of classes in the gesture recognition problem
NUM_CLASSES = 18

# Hardcoded length of the sliding window mechanism employed to segment the data
SLIDING_WINDOW_LENGTH = 24

# Length of the input sequence after convolutional operations
FINAL_SEQUENCE_LENGTH = 8

# Hardcoded step of the sliding window mechanism employed to segment the data
SLIDING_WINDOW_STEP = 12

# Batch Size
BATCH_SIZE = 100

# Number filters convolutional layers
NUM_FILTERS = 64

# Size filters convolutional layers
FILTER_SIZE = 5

# Number of unit in the long short-term recurrent layers
NUM_UNITS_LSTM = 128

这些天我把keras换成keras 2.并且网络没有改变。我的代码如下：

X_train = X_train.reshape((-1, 1, SLIDING_WINDOW_LENGTH, NUM_SENSOR_CHANNELS))
X_test = X_test.reshape((-1, 1, SLIDING_WINDOW_LENGTH, NUM_SENSOR_CHANNELS))

# network
inputs = Input(shape=(1, SLIDING_WINDOW_LENGTH, NUM_SENSOR_CHANNELS))
conv1 = ELU()(
    Conv2D(filters=NUM_FILTERS, kernel_size=(1, FILTER_SIZE), strides=(1, 1), padding='valid', activation='relu',
           kernel_initializer='normal', data_format='channels_last')(inputs))
conv2 = ELU()(
    Conv2D(filters=NUM_FILTERS, kernel_size=(1, FILTER_SIZE), strides=(1, 1), padding='valid', activation='relu',
           kernel_initializer='normal', data_format='channels_last')(conv1))
conv3 = ELU()(
    Conv2D(filters=NUM_FILTERS, kernel_size=(1, FILTER_SIZE), strides=(1, 1), padding='valid', activation='relu',
           kernel_initializer='normal', data_format='channels_last')(conv2))
conv4 = ELU()(
    Conv2D(filters=NUM_FILTERS, kernel_size=(1, FILTER_SIZE), strides=(1, 1), padding='valid', activation='relu',
           kernel_initializer='normal', data_format='channels_last')(conv3))
# permute1 = Permute((2, 1, 3))(conv4)
reshape1 = Reshape((SLIDING_WINDOW_LENGTH - (FILTER_SIZE - 1) * 4, NUM_FILTERS * 1))(conv4)  # 4 for 4 convs
gru1 = GRU(NUM_UNITS_LSTM, return_sequences=True, implementation=0)(reshape1)
gru2 = GRU(NUM_UNITS_LSTM, return_sequences=False, implementation=0)(gru1)  # implementation=2 for GPU
outputs = Dense(NUM_CLASSES, activation='softmax')(gru2)

并且速度似乎更快但形状很奇怪，因为我不知道我的频道在哪里？

我的代码有什么问题，有人可以帮忙吗？ THX

Answer 1

似乎Keras自己处理频道参数。

Keras 2的频道在哪里？

1 个答案: