代码之家 › 专栏 › 技术社区 › user288609

ValueError:输入0与层conv1d\u 1不兼容:预期ndim=3,发现ndim=4

conv-neural-network keras deep-learning neural-network machine-learning

user288609 · 技术社区 · 8 年前

我正在使用Keras提供的conv1d层为层序数据建立预测模型。我就是这样做的

model= Sequential()
model.add(Conv1D(60,32, strides=1, activation='relu',padding='causal',input_shape=(None,64,1)))
model.add(Conv1D(80,10, strides=1, activation='relu',padding='causal'))
model.add(Dropout(0.25))
model.add(Conv1D(100,5, strides=1, activation='relu',padding='causal'))
model.add(MaxPooling1D(1))
model.add(Dropout(0.25))
model.add(Dense(300,activation='relu'))
model.add(Dense(1,activation='relu'))
print(model.summary())

但是,调试信息

Traceback (most recent call last):
File "processing_2a_1.py", line 96, in <module>
model.add(Conv1D(60,32, strides=1, activation='relu',padding='causal',input_shape=(None,64,1)))
File "build/bdist.linux-x86_64/egg/keras/models.py", line 442, in add
File "build/bdist.linux-x86_64/egg/keras/engine/topology.py", line 558, in __call__
File "build/bdist.linux-x86_64/egg/keras/engine/topology.py", line 457, in assert_input_compatibility
ValueError: Input 0 is incompatible with layer conv1d_1: expected ndim=3, found ndim=4

培训数据和验证数据形状如下

('X_train shape ', (1496000, 64, 1))
('Y_train shape ', (1496000, 1))
('X_val shape ', (374000, 64, 1))
('Y_val shape ', (374000, 1))

我认为 input_shape 在第一层中,设置不正确。如何设置?

使现代化 :使用后 input_shape=(64,1) ,即使模型摘要贯穿

________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv1d_1 (Conv1D)            (None, 64, 60)            1980
_________________________________________________________________
conv1d_2 (Conv1D)            (None, 64, 80)            48080
_________________________________________________________________
dropout_1 (Dropout)          (None, 64, 80)            0
_________________________________________________________________
conv1d_3 (Conv1D)            (None, 64, 100)           40100
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 64, 100)           0
_________________________________________________________________
dropout_2 (Dropout)          (None, 64, 100)           0
_________________________________________________________________
dense_1 (Dense)              (None, 64, 300)           30300
_________________________________________________________________
dense_2 (Dense)              (None, 64, 1)             301
=================================================================
Total params: 120,761
Trainable params: 120,761
Non-trainable params: 0
_________________________________________________________________
None
Traceback (most recent call last):
  File "processing_2a_1.py", line 125, in <module>
    history=model.fit(X_train, Y_train, batch_size=batch_size, validation_data=(X_val,Y_val), epochs=nr_of_epochs,verbose=2)
  File "build/bdist.linux-x86_64/egg/keras/models.py", line 871, in fit
  File "build/bdist.linux-x86_64/egg/keras/engine/training.py", line 1524, in fit
  File "build/bdist.linux-x86_64/egg/keras/engine/training.py", line 1382, in _standardize_user_data
  File "build/bdist.linux-x86_64/egg/keras/engine/training.py", line 132, in _standardize_input_data
ValueError: Error when checking target: expected dense_2 to have 3 dimensions, but got array with shape (1496000, 1)

3 回复 | 直到 8 年前

Maxim 8 年前

你要么改变 input_shape 到

input_shape=(64,1)

... 或使用 batch_input_shape :

batch_input_shape=(None, 64, 1)

This discussion 详细解释了keras中两者之间的差异。

Frightera 5 年前

我也有同样的问题。我发现扩展输入数据的维度可以使用 tf.expand_dims

x = expand_dims(x, axis=-1)

Axel Bregnsbo 6 年前

我想用我的箱子 Conv2D 在单个20*32要素地图上,并执行了以下操作:

print(kws_x_train.shape)                     # (8000,20,32)
model = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(16, (3, 8), input_shape=(20,32)),
])
...
model.fit(kws_x_train, kws_y_train, epochs=15)

这使得 expected ndim=4, found ndim=3. Full shape received: [None, 20, 32] . 但是你需要告诉我 Conv2D 只有1个特征映射,并向输入向量添加额外维度。这起到了作用:

kws_x_train2 = kws_x_train.reshape(kws_x_train.shape + (1,))
print(kws_x_train2.shape)                     # (8000,20,32,1)
model = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(16, (3, 8), input_shape=(20,32,1)),
])
...
model.fit(kws_x_train2, kws_y_train, epochs=15)