model= Sequential([
Conv2D(filters=32, kernel_size=3, activation='relu', input_shape= im_shape),
MaxPooling2D(pool_size=2),
Conv2D(filters=16, kernel_size=3, activation='relu', input_shape= im_shape),
MaxPooling2D(pool_size=2),
Flatten(),
Dense(20, activation='relu'),
Dense(2, activation='softmax')
])
model.compile(loss = tensorflow.keras.losses.sparse_categorical_crossentropy,
optimizer =tensorflow.keras.optimizers.Adadelta(), metrics = ['accuracy'])
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)
