from tensorflow.keras.layers import Dense, Dropout, Flatten from tensorflow.keras.layers import Conv2D, MaxPooling2D 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 # %%