Hi!
Could somebody point me to the correct direction?
In the Programming Assignemnt of C2W4, when I test my train_val_generators function, I get this error:
TypeError: object of type ‘ImageDataGenerator’ has no len()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-20-c93bf4854fbc> in <module>()
1 # Test your generators
----> 2 train_generator, validation_generator = train_val_generators(training_images, training_labels, validation_images, validation_labels)
3
4 print(f"Images of training generator have shape: {train_generator.x.shape}")
5 print(f"Labels of training generator have shape: {train_generator.y.shape}")
3 frames
<ipython-input-19-115d113ddc15> in train_val_generators(training_images, training_labels, validation_images, validation_labels)
39 train_generator = train_datagen.flow(x=train_datagen,
40 y=training_labels,
---> 41 batch_size=32) #25
42
43
/usr/local/lib/python3.7/dist-packages/keras/preprocessing/image.py in flow(self, x, y, batch_size, shuffle, sample_weight, seed, save_to_dir, save_prefix, save_format, subset)
894 save_prefix=save_prefix,
895 save_format=save_format,
--> 896 subset=subset)
897
898 def flow_from_directory(self,
/usr/local/lib/python3.7/dist-packages/keras/preprocessing/image.py in __init__(self, x, y, image_data_generator, batch_size, shuffle, sample_weight, seed, data_format, save_to_dir, save_prefix, save_format, subset, dtype)
472 save_format=save_format,
473 subset=subset,
--> 474 **kwargs)
475
476
/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/numpy_array_iterator.py in __init__(self, x, y, image_data_generator, batch_size, shuffle, sample_weight, seed, data_format, save_to_dir, save_prefix, save_format, subset, dtype)
83 x_misc = []
84
---> 85 if y is not None and len(x) != len(y):
86 raise ValueError('`x` (images tensor) and `y` (labels) '
87 'should have the same length. '
TypeError: object of type 'ImageDataGenerator' has no len()
I’m following the same structure we used in the course. This is how I define the function:
# GRADED FUNCTION: train_val_generators
def train_val_generators(training_images, training_labels, validation_images, validation_labels):
"""
Creates the training and validation data generators
Args:
training_images (array): parsed images from the train CSV file
training_labels (array): parsed labels from the train CSV file
validation_images (array): parsed images from the test CSV file
validation_labels (array): parsed labels from the test CSV file
Returns:
train_generator, validation_generator - tuple containing the generators
### START CODE HERE
# In this section you will have to add another dimension to the data
# So, for example, if your array is (10000, 28, 28)
# You will need to make it (10000, 28, 28, 1)
# Hint: np.expand_dims
training_images = np.expand_dims(training_images,3)
validation_images = np.expand_dims(validation_images,3)
# Instantiate the ImageDataGenerator class
# Don't forget to normalize pixel values
# and set arguments to augment the images (if desired)
train_datagen = ImageDataGenerator(
rescale = 1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
# Pass in the appropriate arguments to the flow method
train_generator = train_datagen.flow(x=train_datagen,
y=training_labels,
batch_size=32) #25
# Instantiate the ImageDataGenerator class (don't forget to set the rescale argument)
# Remember that validation data should not be augmented
validation_datagen = ImageDataGenerator(rescale = 1./255)
# Pass in the appropriate arguments to the flow method
validation_generator = validation_datagen.flow(x=validation_datagen,
y=validation_labels,
batch_size=32) #25
### END CODE HERE
return train_generator, validation_generator
Thanks for your attention 