Stuck on an issue?

Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.

And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.

How to solve this problem?

See original GitHub issue

Traceback (most recent call last):
  File "train_liveness.py", line 142, in <module>
    epochs=EPOCHS)
  File "C:\Users\Gepeng-Ji\Anaconda3\envs\keras\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "C:\Users\Gepeng-Ji\Anaconda3\envs\keras\lib\site-packages\keras\engine\training.py", line 1418, in fit_generator
    initial_epoch=initial_epoch)
  File "C:\Users\Gepeng-Ji\Anaconda3\envs\keras\lib\site-packages\keras\engine\training_generator.py", line 144, in fit_generator
    val_x, val_y, val_sample_weight)
  File "C:\Users\Gepeng-Ji\Anaconda3\envs\keras\lib\site-packages\keras\engine\training.py", line 751, in _standardize_user_data
    exception_prefix='input')
  File "C:\Users\Gepeng-Ji\Anaconda3\envs\keras\lib\site-packages\keras\engine\training_utils.py", line 128, in standardize_input_data
    'with shape ' + str(data_shape))
ValueError: Error when checking input: expected input_2 to have 2 dimensions, but got array with shape (78, 2, 2)

Issue Analytics

State:
Created 4 years ago
Comments:15 (7 by maintainers)

Top GitHub Comments

1reaction

4uiiurz1commented, Apr 26, 2019

It’s because that labels are one-hot encoded twice. Please try to remove following line:

labels = np_utils.to_categorical(labels, 2)

1reaction

4uiiurz1commented, Apr 26, 2019

ImageDataGenerator doesn’t support the tuple ([X_train, y_train], y_train)… Please try following code:

class MyImageDataGenerator:
    def __init__(self):
        self.datagen = ImageDataGenerator(rotation_range=20, zoom_range=0.15, brightness_range=[0.5, 1.5],
                         width_shift_range=0.2, height_shift_range=0.2,
                         shear_range=0.15, horizontal_flip=True, fill_mode="nearest")

    def flow(self, x, y=None, batch_size=32, shuffle=True, sample_weight=None,
             seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None):
        batches = self.datagen.flow(x, y, batch_size, shuffle, sample_weight,
                               seed, save_to_dir, save_prefix, save_format, subset)

        while True:
            x_batch, y_batch = next(batches)

            yield [x_batch, y_batch], y_batch

aug = MyImageDataGenerator()


H = model.fit_generator(aug.flow(trainX, trainY, batch_size=BS),
                        validation_data=([testX, testY], testY), steps_per_epoch=len(trainX) // BS,
                        epochs=EPOCHS)

Top Results From Across the Web

Solve - Step-by-Step Math Problem Solver

QuickMath allows students to get instant solutions to all kinds of math problems, from algebra and equation solving right through to calculus and...

How to Solve Problems - Harvard Business Review

First, map out what you remember from each step of your problem-solving. Were all of you on the same page at each stage?...

3 Ways to Solve a Problem - wikiHow

1. Define the problem. Find the real problem, not just the symptoms that result from the problem. When defining the problem, do not...

11 Brilliant Problem-Solving Techniques Nobody Taught You

All you need to do is ask the question “Why” five times. Start with the problem at hand and ask why it happened,...

What is Problem Solving? Steps, Process & Techniques - ASQ

The Problem-Solving Process · 1. Define the problem. Differentiate fact from opinion; Specify underlying causes · 2. Generate alternative solutions. Postpone ...