# max_scale=5, # noise_level=5, # interp='bicubic', # sub_pixel_flow=True) inputs, targets = gen.generate_batch(1000) # 0 prediction error3 = np.mean(np.square(targets - 0), axis=1) # CNN cnn = CNN(split=False, normalize=True) prediction = cnn.predict('checkpoints/normal/step81000.ckpt', inputs) error = np.mean(np.square(prediction - targets), axis=1) # CNN-split cnn2 = CNN(split=True, normalize=True, fully_connected=500) prediction = cnn2.predict('checkpoints/split/step44000.ckpt', inputs) error4 = np.mean(np.square(prediction - targets), axis=1) # FAST+LK fastlk = FastLK(40, True)
# print(probs) # index = np.argmax(index) # word.append("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"[index]) # print(word) # ============================================================================= # repeat test for i in range(patches.shape[0]): t = patches[i] t_unscaled = tf.cast((t + .5) * 255.0, tf.int32) #repeat_img = np.repeat(t_unscaled,2, axis=1) repeat_img = tf.keras.backend.repeat_elements(t_unscaled, 2, axis=1) print(repeat_img.shape) show_img(repeat_img, scaled=False) repeat_img = tf.cast(repeat_img, tf.float32) t_scaled = repeat_img / 255.0 - .5 t_scaled = tf.expand_dims(t_scaled, axis=0) index = cnn.predict(t_scaled) pred_prob = int(np.max(index) * 100) probs.append(pred_prob) index = np.argmax(index) pred = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"[ index] word.append(pred) # ============================================================================= # print(probs) # print(word) # =============================================================================