问题
For certain problems, the validation data can't be a generator, e.g.: TensorBoard histograms:
If printing histograms, validation_data must be provided, and cannot be a generator.
My current code looks like:
image_data_generator = ImageDataGenerator()
training_seq = image_data_generator.flow_from_directory(training_dir)
validation_seq = image_data_generator.flow_from_directory(validation_dir)
testing_seq = image_data_generator.flow_from_directory(testing_dir)
model = Sequential(..)
# ..
model.compile(..)
model.fit_generator(training_seq, validation_data=validation_seq, ..)
How do I provide it as validation_data=(x_test, y_test)
?
回答1:
Update (22/06/2018): Read the answer provided by the OP for a concise and efficient solution. Read mine to understand what's going on.
In python you can get all the generators data using:
data = [x for x in generator]
But, ImageDataGenerators
does not terminate and therefor the approach above would not work. But we can use the same approach with some modifications to work in this case:
data = [] # store all the generated data batches
labels = [] # store all the generated label batches
max_iter = 100 # maximum number of iterations, in each iteration one batch is generated; the proper value depends on batch size and size of whole data
i = 0
for d, l in validation_generator:
data.append(d)
labels.append(l)
i += 1
if i == max_iter:
break
Now we have two lists of tensor batches. We need to reshape them to make two tensors, one for data (i.e X
) and one for labels (i.e. y
):
data = np.array(data)
data = np.reshape(data, (data.shape[0]*data.shape[1],) + data.shape[2:])
labels = np.array(labels)
labels = np.reshape(labels, (labels.shape[0]*labels.shape[1],) + labels.shape[2:])
回答2:
Python 2.7 and Python 3.* solution:
from platform import python_version_tuple
if python_version_tuple()[0] == '3':
xrange = range
izip = zip
imap = map
else:
from itertools import izip, imap
import numpy as np
# ..
# other code as in question
# ..
x, y = izip(*(validation_seq[i] for i in xrange(len(validation_seq))))
x_val, y_val = np.vstack(x), np.vstack(y)
Or to support class_mode='binary'
, then:
from keras.utils import to_categorical
x_val = np.vstack(x)
y_val = np.vstack(imap(to_categorical, y))[:,0] if class_mode == 'binary' else y
Full runnable code: https://gist.github.com/AlecTaylor/7f6cc03ed6c3dd84548a039e2e0fd006
来源:https://stackoverflow.com/questions/50928329/getting-x-test-y-test-from-generator-in-keras