[英]Incompatible shapes error while training ConvNet using Keras+Tensorflow
我正在嘗試構建一個簡單的卷積神經網絡,以將時間序列分為六類之一。 由於形狀不兼容錯誤,我在訓練網絡時遇到問題。
在以下代碼中, n_feats = 1000
, n_classes = 6
。
Fs = 100
input_layer = Input(shape=(None, n_feats), name='input_layer')
conv_layer = Conv1D(filters=32, kernel_size=Fs*4, strides=int(Fs/2), padding='same', activation='relu', name='conv_net_coarse')(input_layer)
conv_layer = MaxPool1D(pool_size=4, name='c_maxp_1')(conv_layer)
conv_layer = Dropout(rate=0.5, name='c_dropo_1')(conv_layer)
output_layer = Dense(n_classes, name='output_layer')(conv_layer)
model = Model(input_layer, output_layer)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
print(model.summary())
這是模型摘要。
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_layer (InputLayer) (None, None, 1000) 0
_________________________________________________________________
conv_net_coarse (Conv1D) (None, None, 32) 12800032
_________________________________________________________________
c_maxp_1 (MaxPooling1D) (None, None, 32) 0
_________________________________________________________________
c_dropo_1 (Dropout) (None, None, 32) 0
_________________________________________________________________
output_layer (Dense) (None, None, 6) 198
=================================================================
Total params: 12,800,230
Trainable params: 12,800,230
Non-trainable params: 0
_________________________________________________________________
None
當我運行model.fit(X_train, Y_train)
,其中X_train
形狀為(30000, 1, 1000)
Y_train
(30000, 1, 1000)
而Y_train
形狀為(30000, 1, 6)
Y_train
(30000, 1, 6)
,我得到了不兼容的形狀錯誤:
InvalidArgumentError (see above for traceback): Incompatible shapes: [32,0,6] vs. [1,6,1]
[[Node: output_layer/add = Add[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](output_layer/Reshape_2, output_layer/Reshape_3)]]
[[Node: metrics_1/acc/Mean/_197 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_637_metrics_1/acc/Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
如果刪除MaxPool1D
和Dropout
圖層,則模型訓練就很好。 我沒有正確指定這些圖層嗎?
任何幫助,將不勝感激!
所以-問題在於兩個事實:
(number_of_examples, timesteps, features)
要素(number_of_examples, timesteps, features)
,其中要素是每個時間步記錄的內容。 這意味着您應該將數據重塑為(number_of_examples, 1000, 1)
因為您的時間序列具有1000個時間步長和1個功能。 Dropout
層之前使用Flatten
。
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