deep-learning - keras 输入层(Nnoe,200,3)，为什么没有？输入有3维，但得到了形状为(200, 3)的数组

Question

model.fit中的acc gyro为(200 * 3)，Input层中的shape为(200 * 3)。为什么会出现这样的问题？检查输入时出错:预期 acc_input 有 3 个维度，但得到形状为 (200, 3) 的数组。这是我的模型的可视化。



这是我的代码:

WIDE = 20
FEATURE_DIM = 30
CHANNEL = 1
CONV_NUM = 64
CONV_LEN = 3
CONV_LEN_INTE = 3#4
CONV_LEN_LAST = 3#5
CONV_NUM2 = 64
CONV_MERGE_LEN = 8
CONV_MERGE_LEN2 = 6
CONV_MERGE_LEN3 = 4
rnn_size=128

acc_input_tensor = Input(shape=(200,3),name = 'acc_input')
gyro_input_tensor = Input(shape=(200,3),name= 'gyro_input')
Acc_input_tensor = Reshape(target_shape=(20,30,1))(acc_input_tensor)
Gyro_input_tensor = Reshape(target_shape=(20,30,1))(gyro_input_tensor)
acc_conv1 = Conv2D(CONV_NUM,(1, 1*3*CONV_LEN),strides=     (1,1*3),padding='valid',activation=None)(Acc_input_tensor)
acc_conv1 = BatchNormalization(axis=1)(acc_conv1)
acc_conv1 = Activation('relu')(acc_conv1)
acc_conv1 = Dropout(0.2)(acc_conv1)
acc_conv2 = Conv2D(CONV_NUM,(1,CONV_LEN_INTE),strides= (1,1),padding='valid',activation=None)(acc_conv1)
acc_conv2 = BatchNormalization(axis=1)(acc_conv2)
acc_conv2 = Activation('relu')(acc_conv2)
acc_conv2 = Dropout(0.2)(acc_conv2)

acc_conv3 = Conv2D(CONV_NUM,(1,CONV_LEN_LAST),strides=(1,1),padding='valid',activation=None)(acc_conv2)
acc_conv3 = BatchNormalization(axis=1)(acc_conv3)
acc_conv3 = Activation('relu')(acc_conv3)
acc_conv3 = Dropout(0.2)(acc_conv3)
gyro_conv1 = Conv2D(CONV_NUM,(1, 1*3*CONV_LEN),strides=(1,1*3),padding='valid',activation=None)(Gyro_input_tensor)
gyro_conv1 = BatchNormalization(axis=1)(gyro_conv1)
gyro_conv1 = Activation('relu')(gyro_conv1)
gyro_conv1 = Dropout(0.2)(gyro_conv1)

gyro_conv2 = Conv2D(CONV_NUM,(1, CONV_LEN_INTE),strides=(1,1),padding='valid',activation=None)(gyro_conv1)
gyro_conv2 = BatchNormalization(axis=1)(gyro_conv2)
gyro_conv2 = Activation('relu')(gyro_conv2)
gyro_conv2 = Dropout(0.2)(gyro_conv2)

gyro_conv3 = Conv2D(CONV_NUM,(1, CONV_LEN_LAST),strides=(1,1),padding='valid',activation=None)(gyro_conv2)
gyro_conv3 = BatchNormalization(axis=1)(gyro_conv3)
gyro_conv3 = Activation('relu')(gyro_conv3)
gyro_conv3 = Dropout(0.2)(gyro_conv3)
sensor_conv_in = concatenate([acc_conv3, gyro_conv3], 2)
sensor_conv_in = Dropout(0.2)(sensor_conv_in)
sensor_conv1 = Conv2D(CONV_NUM2,kernel_size=(2, CONV_MERGE_LEN),padding='SAME')(sensor_conv_in)
sensor_conv1 = BatchNormalization(axis=1)(sensor_conv1)
sensor_conv1 = Activation('relu')(sensor_conv1)
sensor_conv1 = Dropout(0.2)(sensor_conv1)
sensor_conv2 = Conv2D(CONV_NUM2,kernel_size=(2, CONV_MERGE_LEN2),padding='SAME')(sensor_conv1)
sensor_conv2 = BatchNormalization(axis=1)(sensor_conv2)
sensor_conv2 = Activation('relu')(sensor_conv2)
sensor_conv2 = Dropout(0.2)(sensor_conv2)

sensor_conv3 = Conv2D(CONV_NUM2,kernel_size=(2, CONV_MERGE_LEN3),padding='SAME')(sensor_conv2)
sensor_conv3 = BatchNormalization(axis=1)(sensor_conv3)
sensor_conv3 = Activation('relu')(sensor_conv3)

conv_shape = sensor_conv3.get_shape()
print conv_shape
x1 = Reshape(target_shape=(int(conv_shape[1]), int(conv_shape[2]*conv_shape[3])))(sensor_conv3)

x1 = Dense(64, activation='relu')(x1)

gru_1 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru1')(x1)
gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, init='he_normal', name='gru1_b')(x1)
gru1_merged = merge([gru_1, gru_1b], mode='sum')

gru_2 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru2')(gru1_merged)
gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, init='he_normal', name='gru2_b')(gru1_merged)
x = merge([gru_2, gru_2b], mode='concat')
x = Dropout(0.25)(x)
n_class=2
x = Dense(n_class)(x)
model = Model(input=[acc_input_tensor,gyro_input_tensor], output=x)
model.compile(loss='mean_squared_error',optimizer='adam')
model.fit(inputs=[acc,gyro],outputs=labels,batch_size=1, validation_split=0.2, epochs=2,verbose=1 ,
      shuffle=False)

model.fit中的acc gyro为(200 * 3)，Input层中的shape为(200 * 3)。为什么会出现这样的问题？检查输入时出错:预期 acc_input 有 3 个维度，但得到形状为 (200, 3) 的数组

Answer 1

形状(None, 200, 3)在 Keras 中用于批量处理，None意味着 batch_size ，因为在创建或 reshape 输入数组时，批次大小可能未知，因此如果您将使用 batch_size = 128您的批量输入矩阵将具有形状 (128, 200, 3)