print(model.summary())
# %%
history = model.fit(
np.array(X_train), np.array(y_train),
batch_size = 100,
epochs = 50, verbose=2, validation_data = (X_test, y_test),
)
# %% [markdown]
# ### CNN Predict
# %%
score = model.evaluate(X_test, y_test, verbose = 0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
pred = model.predict(X_test)
pred = np.argmax(pred, axis = 1)
label = (y_test)
print(pred)
print(label)
# %% [markdown]
# ## Color Clustering with K-Means
# ### Image compression using K-Means can be easy.
