Input 0 is incompatible with layer flatten_5: expected min_ndim=3, found ndim=2

Question

I am trying to fine-tune VGG16 neural network, here is the code:

vgg16_model = VGG16(weights="imagenet", include_top="false", input_shape=(224,224,3))
model = Sequential()
model.add(vgg16_model)
#add fully connected layer:
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax')) 

I am getting this error:

ValueError Traceback (most recent call last) in

2 model.add(vgg16_model)

3 #add fully connected layer:

----> 4 model.add(Flatten())

5 model.add(Dense(256, activation='relu'))

6 model.add(Dropout(0.5))

/usr/local/anaconda/lib/python3.6/site-packages/keras/engine/sequential.py in add(self, layer) 179 self.inputs = network.get_source_inputs(self.outputs[0])

180 elif self.outputs:

--> 181 output_tensor = layer(self.outputs[0])

182 if isinstance(output_tensor, list):

183 raise TypeError('All layers in a Sequential model '

/usr/local/anaconda/lib/python3.6/site-packages/keras/engine/base_layer.py in call(self, inputs, **kwargs)

412 # Raise exceptions in case the input is not compatible

413 # with the input_spec specified in the layer constructor.

--> 414 self.assert_input_compatibility(inputs)

415

416 # Collect input shapes to build layer.

/usr/local/anaconda/lib/python3.6/site-packages/keras/engine/base_layer.py in assert_input_compatibility(self, inputs)

325 self.name + ': expected min_ndim=' +

326 str(spec.min_ndim) + ', found ndim=' +

--> 327 str(K.ndim(x)))

328 # Check dtype.

329 if spec.dtype is not None:

ValueError: Input 0 is incompatible with layer flatten_5: expected min_ndim=3, found ndim=2

I tried many suggested solutions but none of them could solve my problem. How can I solve this?

Answer 1

In officially keras webpage, on

Fine-tune InceptionV3 on a new set of classes

from keras.models import Model
vgg16_model = VGG16(weights="imagenet", include_top="false", input_shape=(224,224,3))
x = vgg16_model.output
x=Flatten()(x)
x=Dense(256, activation='relu')(x)
x=Dropout(0.5)(x)
predictions=Dense(3, activation='softmax')(x)

model = Model(inputs=base_model.input, outputs=predictions)

You have an error in include_top="false", this causes you the error message. Try:

vgg16_model = VGG16(weights="imagenet", include_top=False, input_shape=(224,224,3))