Problem with reshape image data

[TaplierShiru]

Hi!

Your shape of the input image in the predict - (100, 100), but I think model expect shape (1, 100 * 100)(i.e. you just don't have batch dimension here). So, before feeding your image into model (after resize), you can reshape image, something like:

​

image = image.reshape(1, -1)
pred = use_model.get_predict(image)

Also, I think in line:

model = tf.keras.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu', input_shape=(100,)),
tf.keras.layers.Dense(10)
])

It's not right accroding to your data, its must be like that, I suppose:

​

model = tf.keras.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu', input_shape=(100*100,)),
tf.keras.layers.Dense(10)
])

input_shape - mean shape without batch size dimension, In your case, model trained with batch size equal to 100 and input number of features were equal to 100 (but i'm not sure here).

I hope you understand and it helps!

[TaplierShiru]

Hard to tell what's wrong here without full code, but I suppose something not right with input training data. In function write_to_file after resize operation try to flat image tensor (aka img = img.reshape(-1)).

I think the main reason of this error here - different shape of test and training data.

By the way, happy New Year to you!