问题
I have written a generator function with Keras, before returning X,y from __getitem__ I have double check the shapes of the X's and Y's and they are alright, but generator is giving dimension mismatch array and warnings.
(Colab Code to reproduce: https://colab.research.google.com/drive/1bSJm44MMDCWDU8IrG2GXKBvXNHCuY70G?usp=sharing)
My training and validation generators are pretty much same as
class ValidGenerator(Sequence):
def __init__(self, df, batch_size=64):
self.batch_size = batch_size
self.df = df
self.indices = self.df.index.tolist()
self.num_classes = num_classes
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
return int(len(self.indices) // self.batch_size)
def __getitem__(self, index):
index = self.index[index * self.batch_size:(index + 1) * self.batch_size]
batch = [self.indices[k] for k in index]
X, y = self.__get_data(batch)
return X, y
def on_epoch_end(self):
self.index = np.arange(len(self.indices))
if self.shuffle == True:
np.random.shuffle(self.index)
def __get_data(self, batch):
#some logic is written here
#hat prepares 3 X features and 3 Y outputs
X = [input_array_1,input_array_2,input_array_3]
y = [out_1,out_2,out_3]
#print(len(X))
return X, y
I am return tupple of X,y from which has 3 input features and 3 output features each, so shape of X is (3,32,10,1)
I am using functional api to build model(I have things like concatenation, multi input/output, which isnt possible with sequential) with following structure
When I try to fit the model with generator with following code
train_datagen = TrainGenerator(df=train_df, batch_size=32, num_classes=None, shuffle=True)
valid_datagen = ValidGenerator(df=train_df, batch_size=32, num_classes=None, shuffle=True)
model.fit(train_datagen, epochs=2,verbose=1,callbacks=[checkpoint,es])
I get these warnings and errors, that dont go away
Epoch 1/2 WARNING:tensorflow:Model was constructed with shape (None, 10) for input >Tensor("input_1:0", shape=(None, 10), dtype=float32), but it was called >on an input with incompatible shape (None, None, None).
WARNING:tensorflow:Model was constructed with shape (None, 10) for input Tensor("input_2:0", shape=(None, 10), dtype=float32), but it was called on an input with incompatible shape (None, None, None). WARNING:tensorflow:Model was constructed with shape (None, 10) for input Tensor("input_3:0", shape=(None, 10), dtype=float32), but it was called on an input with incompatible shape (None, None, None). ... ... call return super(RNN, self).call(inputs, **kwargs) /home/eduardo/.virtualenvs/kgpu3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:975 call input_spec.assert_input_compatibility(self.input_spec, inputs, /home/eduardo/.virtualenvs/kgpu3/lib/python3.8/site-packages/tensorflow/python/keras/engine/input_spec.py:176 assert_input_compatibility raise ValueError('Input ' + str(input_index) + ' of layer ' +
ValueError: Input 0 of layer lstm is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, None, None, 88]
I have rechecked whole code and it isnt possible to have input (None,None,None) like in warning or in error, my input dimension is (3,32,10,1)
Update
I have also tried to write a generator function with python and got exactly same error.
My generator function
def generate_arrays_from_file(batchsize,df):
#print(bat)
inputs = []
targets = []
batchcount = 0
while True:
df3 = df.loc[np.arange(batchcount*batchsize,(batchcount*batchsize)+batchsize)]
#Some pre processing
X = [input_array_1,input_array_2,input_array_3]
y = [out_1,out_2,out_3]
yield X,y
batchcount = batchcount +1
It seems like it is something wrong internally wit keras (may be due to the fact I am using functional API)
Update 2
I also tried to output tuple
X = (input1_X,input2_X,input3_X)
y = (output1_y,output2_y,output3_y)
and also named input/output, but it doesnt work
X = {"input_1": input1_X, "input_2": input2_X,"input_3": input3_X}
y = {"output_1": output1_y, "output_2": output2_y,"output_3": output3_y}
Note about problem formulation:
Changing the individual X features to shape (32,10) instead of (32,10,1) might help to get rid of this error but that is not what I want, it changes my problem(I no longer have 10 time steps with one feature each)
回答1:
Keras use 'None' for dynamic dimensions.
As you can see on the model.summary() chart - the model expecting shape(None, 10) for all of your inputs, which is two dimensional. With batch dimension - you should feed three dimensional data to the model.
But you are feeding four dimensional data.
I would guess that your model doesn't split your input list by three inputs. Try to change your inputs to tuple:
X = (input_array_1,input_array_2,input_array_3)
回答2:
In order to resolve this error:
ValueError: Input 0 of layer lstm is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, None, None, 88]
TrainGenerator should be changed in the following way. Current code:
input1_X = np.array(df3['input1_X'].to_list()).reshape(dlen,pad_len,1)
input2_X = np.array(df3['input2_X'].to_list()).reshape(dlen,pad_len,1)
input3_X = np.array(df3['input3_X'].to_list()).reshape(dlen,pad_len,1)
Should be changed to:
input1_X = np.array(df3['input1_X'].to_list()).reshape(dlen,pad_len)
input2_X = np.array(df3['input2_X'].to_list()).reshape(dlen,pad_len)
input3_X = np.array(df3['input3_X'].to_list()).reshape(dlen,pad_len)
The reason is that each of the 3 Inputs expects a 2-dimensional array, but the generator provides a 3-dimensional one. The expected shape is (batch_size, 10).
来源：https://stackoverflow.com/questions/64811632/keras-custom-data-generator-giving-dimension-errors-with-multi-input-and-multi-o