I'm trying to load the bottleneck_features that I obtained from running resnet50 into a top layer model. I ran predict_generator on resnet and saved the resultant bottleneck_features to a npy file. I am unable to fit the model I have created because of the following error:
Traceback (most recent call last):
File "Labeled_Image_Recognition.py", line 119, in <module>
callbacks=[checkpointer])
File "/home/dillon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/models.py", line 963, in fit
validation_steps=validation_steps)
File "/home/dillon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/engine/training.py", line 1630, in fit
batch_size=batch_size)
File "/home/dillon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/engine/training.py", line 1490, in _standardize_user_data
_check_array_lengths(x, y, sample_weights)
File "/home/dillon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/engine/training.py", line 220, in _check_array_lengths
'and ' + str(list(set_y)[0]) + ' target samples.')
ValueError: Input arrays should have the same number of samples as target arrays. Found 940286 input samples and 14951 target samples.
I'm not really sure what it means. I have 940286 total images in my train dir and there are 14951 total subdirs that these images are separated into. My two hypotheses are:
Any guidance into the right direction would be much appreciated!
Here is the code:
# Constants
num_train_dirs = 14951 #This is the total amount of classes I have
num_valid_dirs = 13168
def load_labels(path):
targets = os.listdir(path)
labels = np_utils.to_categorical(targets, len(targets))
return labels
def create_model(train_data):
model = Sequential()
model.add(Flatten(input_shape=train_data.shape[1:]))
model.add(Dense(num_train_dirs, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_train_dirs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
return model
train_data = np.load(open('bottleneck_features/bottleneck_features_train.npy', 'rb'))
train_labels = load_labels(raid_train_dir)
valid_data = np.load(open('bottleneck_features/bottleneck_features_valid.npy', 'rb'))
valid_labels = train_labels
model = create_model(train_data)
model.summary()
checkpointer = ModelCheckpoint(filepath='weights/first_try.hdf5', verbose=1, save_best_only=True)
print("Fitting model...")
model.fit(train_data, train_labels,
epochs=50,
batch_size=100,
verbose=1,
validation_data=(valid_data, valid_labels),
callbacks=[checkpointer])
In case of supervised learning the number of input samples ( X
) must match the number of output (labels) samples ( Y
).
For example: if we want to fit (learn) a NN to recognize handwritten digits and we feed 10.000 images ( X
) to our model, then we should also pass 10.000 labels ( Y
).
In your case those numbers don't match.
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