Keras CNN overfitting for more than four classes - tensorflow

Question

I'm trying to train a classifier on Google QuickDraw drawings using Keras:
import numpy as np
from tensorflow.keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=5, data_format="channels_last", activation="relu", input_shape=(28, 28, 1)))
model.add(MaxPooling2D(data_format="channels_last"))
model.add(Conv2D(filters=16, kernel_size=3, data_format="channels_last", activation="relu"))
model.add(MaxPooling2D(data_format="channels_last"))
model.add(Flatten(data_format="channels_last"))
model.add(Dense(units=128, activation="relu"))
model.add(Dense(units=64, activation="relu"))
model.add(Dense(units=4, activation="softmax"))
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
x = np.load("./x.npy")
y = np.load("./y.npy")
model.fit(x=x, y=y, batch_size=100, epochs=40, validation_split=0.2)
The input data is a 4d array with 12000 normalized images (28 x 28 x 1) per class. The output data is an array of one hot encoded vectors.
If I train this model on four classes, it produces convincing results:
(red is training data, blue is validation data)
I know the model is slightly overfitted. However, I want to keep the architecture as simple as possible, so I accepted that.
My problem is that as soon as I add just one arbitrary class, the model starts to overfit extremely:
I tried many different things to prevent it from overfitting such as Batch Normalization, Dropout, Kernel Regularizers, much more training data and different batch sizes, none of which caused any significant improvement.
What could be the reason why my CNN overfits so much?
EDIT: This is the code I used to create x.npy and y.npy:
import numpy as np
from tensorflow.keras.utils import to_categorical
files = ['cat.npy', 'dog.npy', 'apple.npy', 'banana.npy', 'flower.npy']
SAMPLES = 12000
x = np.concatenate([np.load(f'./data/{f}')[:SAMPLES] for f in files]) / 255.0
y = np.concatenate([np.full(SAMPLES, i) for i in range(len(files))])
# (samples, rows, cols, channels)
x = x.reshape(x.shape[0], 28, 28, 1).astype('float32')
y = to_categorical(y)
np.save('./x.npy', x)
np.save('./y.npy', y)
The .npy files come from here.

Answer 1

The problem lies with how the data split is done. Notice that there are 5 classes and you do 0.2 validation split. By default there's no shuffling and in your code you feed the data in a sequential order. What that means:
Training data consists entirely of 4 classes: 'cat.npy', 'dog.npy', 'apple.npy', 'banana.npy'. That's the 0.8 training split.
Test data is 'flower.npy'. That's your 0.2 validation split. The model was never trained on this so it gets terrible accuracy.
Such results are only possible thanks to the fact that the validation_split=0.2, so you get close to perfect class separation.
Solution
x = np.load("./x.npy")
y = np.load("./y.npy")
# Shuffle the data!
p = np.random.permutation(len(x))
x = x[p]
y = y[p]
model.fit(x=x, y=y, batch_size=100, epochs=40, validation_split=0.2)
if my hypothesis is correct, setting the validation_split to e.g. 0.5 should also get you much better results (though it's not a solution).