I would like to know how to make a minimal and reproducible deep learning example for Stack Overflow. I want to make sure that people have enough information to pinpoint the exact problem with my code. Is it enough to just provide the traceback?

    c:\users\samuel\appdata\local\programs\python\python35\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)135                             ': expected ' + names[i] + ' to have shape ' +136                             str(shape) + ' but got array with shape ' +--> 137                             str(data_shape))138     return data139 

Or should I simply post the error message?

Value Error: Error when checking input: expected dense_1_input to have shape(4,) but got array with shape (1,)

Here are a few tips to make a reproducible, minimal deep learning Example. It's good advice whether it be for Keras, Pytorch, or Tensorflow.

• We can't use your data, but in most cases, it doesn't matter. All we need is the right shape.
  • Use randomly generated numbers of the right shape.
    • E.g., np.random.randint(0, 256, (100, 30, 30, 3) for 100 colored pictures of size 30x30
    • E.g., np.random.choice(np.arange(10), 100) for 100 samples of 10 categories
• We don't need to see your entire pipeline.
  • Only provide the bare minimum to run your code.
• Make the most out of Keras and its debugging abilities.
  • Include the traceback. It will most likely point out the exact problem.
• Neural networks are all about fitting the right shapes.
  • At a minimum, always provide the input shapes.
• Make it easy to test and reproduce.
  • Post your entire neural network architecture.
  • Include your library imports. Define all variables.

Here is an example of a perfect minimal and reproducible example:

"I have an error. When I run this code, it gives me this error:"

ValueError: Error when checking target: expected dense_2 to have shape (10,) but got array with shape

"Here is my architecture, with generated data:"

import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2Dxtrain, xtest = np.random.rand(2, 1000, 30, 30, 3)
ytrain, ytest = np.random.choice(np.arange(10), 2000).reshape(2, 1000) model = Sequential([Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=xtrain.shape[1:]),Conv2D(64, (3, 3), activation='relu'),MaxPooling2D(pool_size=(2, 2)),Flatten(),Dense(128, activation='relu'),Dense(10, activation='softmax')])model.compile(loss=keras.losses.categorical_crossentropy,optimizer=keras.optimizers.Adam(),metrics=['accuracy'])model.fit(xtrain, ytrain,batch_size=16,epochs=10,validation_data=(xtest, ytest))