hi I am building a image classifier for one-class classification in which i’ve used autoencoder while running this model I am getting this error by this line (autoencoder_model.fit) (ValueError: Error when checking target: expected model_2 to have shape (None, 252, 252, 1) but got array with shape (300, 128, 128, 3).)
num_of_samples = img_data.shape[0]
labels = np.ones((num_of_samples,),dtype='int64')
labels[0:376]=0
names = ['cats']
input_shape=img_data[0].shape
X_train, X_test = train_test_split(img_data, test_size=0.2, random_state=2)
inputTensor = Input(input_shape)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(inputTensor)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded_data = MaxPooling2D((2, 2), padding='same')(x)
encoder_model = Model(inputTensor,encoded_data)
# at this point the representation is (4, 4, 8) i.e. 128-dimensional
encoded_input = Input((4,4,8))
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded_input)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu',padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded_data = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
decoder_model = Model(encoded_input,decoded_data)
autoencoder_input = Input(input_shape)
encoded = encoder_model(autoencoder_input)
decoded = decoder_model(encoded)
autoencoder_model = Model(autoencoder_input, decoded)
autoencoder_model.compile(optimizer='adadelta',
`enter code here`loss='binary_crossentropy')
autoencoder_model.fit(X_train, X_train,
epochs=50,
batch_size=32,
validation_data=(X_test, X_test),
callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
Answers:
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Method 1
It’s a simple incompatibility between the output shape of the decoder and the shape of your training data. (Target means output).
I see you’ve got 2 MaxPoolings (dividing your image size by 4), and three upsamplings (multiplying the decoder’s input by 8).
The final output of the autoencoder is too big and doesn’t match your data. You must simply work in the model to make the output shape match your training data.
Method 2
You’re using wrong API
autoencoder_model.fit(X_train, X_train, <--- This one is wrong
epochs=50,
batch_size=32,
validation_data=(X_test, X_test),
callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
Take a look at .fit method source code
from https://github.com/keras-team/keras/blob/master/keras/models.py
def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
**kwargs):
"""Trains the model for a fixed number of epochs (iterations on a dataset).
# Arguments
x: Numpy array of training data.
If the input layer in the model is named, you can also pass a
dictionary mapping the input name to a Numpy array.
`x` can be `None` (default) if feeding from
framework-native tensors (e.g. TensorFlow data tensors).
y: Numpy array of target (label) data.
If the output layer in the model is named, you can also pass a
dictionary mapping the output name to a Numpy array.
`y` can be `None` (default) if feeding from
framework-native tensors (e.g. TensorFlow data tensors).
So the x should be data, and the y should be label of the data.
Hope that help
All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0