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model.fit中的acc陀螺是（200 * 3）輸入層形狀是（200 * 3）。爲什麼會有這樣的問題？檢查輸入時出錯：期望的acc_input有3個維度，但有形狀的數組（200，3）。這是我的模型的可視化。keras輸入層（Nnoe，200,3），爲什麼沒有？輸入有3個維度，但得到了形狀的陣列（200,3）
這是我的代碼：
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)
在model.fit在ACC陀螺是（200×3），在輸入層形狀是（200×3）。爲什麼會有這樣的問題？檢查的輸入錯誤：預期acc_input有3個維度，但得到了陣列狀（200，3）
對此問題感到抱歉，但是您是如何形象化您的模型的？ – Paddy
這是一個deeeeep網絡，希望你擁有大量的數據 – DJK