Epoch 1/100 1/1 – 19s – loss: 1.7918 – val_loss: 1.7952 – 19s/epoch – 19s/step Epoch 2/100 1/1 – 0s – loss: 1.7899 – val_loss: 1.7981 – 78ms/epoch – 78ms/step Epoch 3/100 1/1 – 0s – loss: 1.7881 – val_loss: 1.8010 – 75ms/epoch – 75ms/step Epoch 4/100 1/1 – 0s – loss: 1.7862 – val_loss: 1.8040 – 91ms/epoch – 91ms/step Epoch 5/100 1/1 – 0s – loss: 1.7844 – val_loss: 1.8071 – 64ms/epoch – 64ms/step Epoch 6/100 1/1 – 0s – loss: 1.7825 – val_loss: 1.8102 – 71ms/epoch – 71ms/step Epoch 7/100 1/1 – 0s – loss: 1.7805 – val_loss: 1.8134 – 87ms/epoch – 87ms/step Epoch 8/100 1/1 – 0s – loss: 1.7785 – val_loss: 1.8167 – 81ms/epoch – 81ms/step Epoch 9/100 1/1 – 0s – loss: 1.7764 – val_loss: 1.8200 – 89ms/epoch – 89ms/step Epoch 10/100 1/1 – 0s – loss: 1.7743 – val_loss: 1.8235 – 49ms/epoch – 49ms/step Epoch 11/100 1/1 – 0s – loss: 1.7722 – val_loss: 1.8270 – 75ms/epoch – 75ms/step Epoch 12/100 1/1 – 0s – loss: 1.7699 – val_loss: 1.8306 – 86ms/epoch – 86ms/step Epoch 13/100 1/1 – 0s – loss: 1.7676 – val_loss: 1.8344 – 173ms/epoch – 173ms/step Epoch 14/100 1/1 – 0s – loss: 1.7652 – val_loss: 1.8382 – 69ms/epoch – 69ms/step Epoch 15/100 1/1 – 0s – loss: 1.7627 – val_loss: 1.8422 – 65ms/epoch – 65ms/step Epoch 16/100 1/1 – 0s – loss: 1.7601 – val_loss: 1.8463 – 100ms/epoch – 100ms/step Epoch 17/100 1/1 – 0s – loss: 1.7574 – val_loss: 1.8505 – 83ms/epoch – 83ms/step Epoch 18/100 1/1 – 0s – loss: 1.7545 – val_loss: 1.8548 – 54ms/epoch – 54ms/step Epoch 19/100 1/1 – 0s – loss: 1.7516 – val_loss: 1.8593 – 82ms/epoch – 82ms/step Epoch 20/100 1/1 – 0s – loss: 1.7485 – val_loss: 1.8639 – 59ms/epoch – 59ms/step Epoch 21/100 1/1 – 0s – loss: 1.7453 – val_loss: 1.8687 – 63ms/epoch – 63ms/step Epoch 22/100 1/1 – 0s – loss: 1.7420 – val_loss: 1.8736 – 46ms/epoch – 46ms/step Epoch 23/100 1/1 – 0s – loss: 1.7384 – val_loss: 1.8787 – 38ms/epoch – 38ms/step Epoch 24/100 1/1 – 0s – loss: 1.7348 – val_loss: 1.8840 – 48ms/epoch – 48ms/step Epoch 25/100 1/1 – 0s – loss: 1.7309 – val_loss: 1.8894 – 45ms/epoch – 45ms/step Epoch 26/100 1/1 – 0s – loss: 1.7269 – val_loss: 1.8950 – 38ms/epoch – 38ms/step Epoch 27/100 1/1 – 0s – loss: 1.7226 – val_loss: 1.9009 – 51ms/epoch – 51ms/step Epoch 28/100 1/1 – 0s – loss: 1.7182 – val_loss: 1.9069 – 77ms/epoch – 77ms/step Epoch 29/100 1/1 – 0s – loss: 1.7135 – val_loss: 1.9131 – 56ms/epoch – 56ms/step Epoch 30/100 1/1 – 0s – loss: 1.7086 – val_loss: 1.9195 – 60ms/epoch – 60ms/step Epoch 31/100 1/1 – 0s – loss: 1.7034 – val_loss: 1.9262 – 55ms/epoch – 55ms/step Epoch 32/100 1/1 – 0s – loss: 1.6980 – val_loss: 1.9330 – 63ms/epoch – 63ms/step Epoch 33/100 1/1 – 0s – loss: 1.6922 – val_loss: 1.9402 – 51ms/epoch – 51ms/step Epoch 34/100 1/1 – 0s – loss: 1.6862 – val_loss: 1.9475 – 56ms/epoch – 56ms/step Epoch 35/100 1/1 – 0s – loss: 1.6799 – val_loss: 1.9551 – 56ms/epoch – 56ms/step Epoch 36/100 1/1 – 0s – loss: 1.6732 – val_loss: 1.9629 – 54ms/epoch – 54ms/step Epoch 37/100 1/1 – 0s – loss: 1.6661 – val_loss: 1.9710 – 69ms/epoch – 69ms/step Epoch 38/100 1/1 – 0s – loss: 1.6587 – val_loss: 1.9794 – 70ms/epoch – 70ms/step Epoch 39/100 1/1 – 0s – loss: 1.6508 – val_loss: 1.9880 – 52ms/epoch – 52ms/step Epoch 40/100 1/1 – 0s – loss: 1.6426 – val_loss: 1.9970 – 58ms/epoch – 58ms/step Epoch 41/100 1/1 – 0s – loss: 1.6338 – val_loss: 2.0062 – 58ms/epoch – 58ms/step Epoch 42/100 1/1 – 0s – loss: 1.6246 – val_loss: 2.0157 – 67ms/epoch – 67ms/step Epoch 43/100 1/1 – 0s – loss: 1.6149 – val_loss: 2.0256 – 52ms/epoch – 52ms/step Epoch 44/100 1/1 – 0s – loss: 1.6046 – val_loss: 2.0357 – 54ms/epoch – 54ms/step Epoch 45/100 1/1 – 0s – loss: 1.5937 – val_loss: 2.0462 – 69ms/epoch – 69ms/step Epoch 46/100 1/1 – 0s – loss: 1.5823 – val_loss: 2.0570 – 71ms/epoch – 71ms/step Epoch 47/100 1/1 – 0s – loss: 1.5702 – val_loss: 2.0681 – 62ms/epoch – 62ms/step Epoch 48/100 1/1 – 0s – loss: 1.5574 – val_loss: 2.0796 – 66ms/epoch – 66ms/step Epoch 49/100 1/1 – 0s – loss: 1.5440 – val_loss: 2.0915 – 78ms/epoch – 78ms/step Epoch 50/100 1/1 – 0s – loss: 1.5298 – val_loss: 2.1037 – 63ms/epoch – 63ms/step Epoch 51/100 1/1 – 0s – loss: 1.5148 – val_loss: 2.1163 – 74ms/epoch – 74ms/step Epoch 52/100 1/1 – 0s – loss: 1.4990 – val_loss: 2.1293 – 84ms/epoch – 84ms/step Epoch 53/100 1/1 – 0s – loss: 1.4824 – val_loss: 2.1426 – 84ms/epoch – 84ms/step Epoch 54/100 1/1 – 0s – loss: 1.4650 – val_loss: 2.1563 – 129ms/epoch – 129ms/step Epoch 55/100 1/1 – 0s – loss: 1.4466 – val_loss: 2.1705 – 89ms/epoch – 89ms/step Epoch 56/100 1/1 – 0s – loss: 1.4273 – val_loss: 2.1850 – 70ms/epoch – 70ms/step Epoch 57/100 1/1 – 0s – loss: 1.4071 – val_loss: 2.1999 – 90ms/epoch – 90ms/step Epoch 58/100 1/1 – 0s – loss: 1.3858 – val_loss: 2.2152 – 98ms/epoch – 98ms/step Epoch 59/100 1/1 – 0s – loss: 1.3636 – val_loss: 2.2309 – 95ms/epoch – 95ms/step Epoch 60/100 1/1 – 0s – loss: 1.3405 – val_loss: 2.2469 – 62ms/epoch – 62ms/step Epoch 61/100 1/1 – 0s – loss: 1.3163 – val_loss: 2.2634 – 68ms/epoch – 68ms/step Epoch 62/100 1/1 – 0s – loss: 1.2911 – val_loss: 2.2802 – 74ms/epoch – 74ms/step Epoch 63/100 1/1 – 0s – loss: 1.2649 – val_loss: 2.2973 – 53ms/epoch – 53ms/step Epoch 64/100 1/1 – 0s – loss: 1.2378 – val_loss: 2.3148 – 67ms/epoch – 67ms/step Epoch 65/100 1/1 – 0s – loss: 1.2098 – val_loss: 2.3326 – 72ms/epoch – 72ms/step Epoch 66/100 1/1 – 0s – loss: 1.1809 – val_loss: 2.3507 – 54ms/epoch – 54ms/step Epoch 67/100 1/1 – 0s – loss: 1.1512 – val_loss: 2.3690 – 66ms/epoch – 66ms/step Epoch 68/100 1/1 – 0s – loss: 1.1207 – val_loss: 2.3876 – 63ms/epoch – 63ms/step Epoch 69/100 1/1 – 0s – loss: 1.0895 – val_loss: 2.4063 – 60ms/epoch – 60ms/step Epoch 70/100 1/1 – 0s – loss: 1.0577 – val_loss: 2.4252 – 56ms/epoch – 56ms/step Epoch 71/100 1/1 – 0s – loss: 1.0255 – val_loss: 2.4442 – 65ms/epoch – 65ms/step Epoch 72/100 1/1 – 0s – loss: 0.9928 – val_loss: 2.4633 – 82ms/epoch – 82ms/step Epoch 73/100 1/1 – 0s – loss: 0.9599 – val_loss: 2.4823 – 66ms/epoch – 66ms/step Epoch 74/100 1/1 – 0s – loss: 0.9268 – val_loss: 2.5014 – 59ms/epoch – 59ms/step Epoch 75/100 1/1 – 0s – loss: 0.8936 – val_loss: 2.5204 – 87ms/epoch – 87ms/step Epoch 76/100 1/1 – 0s – loss: 0.8606 – val_loss: 2.5393 – 76ms/epoch – 76ms/step Epoch 77/100 1/1 – 0s – loss: 0.8278 – val_loss: 2.5580 – 44ms/epoch – 44ms/step Epoch 78/100 1/1 – 0s – loss: 0.7953 – val_loss: 2.5765 – 49ms/epoch – 49ms/step Epoch 79/100 1/1 – 0s – loss: 0.7634 – val_loss: 2.5948 – 61ms/epoch – 61ms/step Epoch 80/100 1/1 – 0s – loss: 0.7322 – val_loss: 2.6129 – 59ms/epoch – 59ms/step Epoch 81/100 1/1 – 0s – loss: 0.7017 – val_loss: 2.6306 – 49ms/epoch – 49ms/step Epoch 82/100 1/1 – 0s – loss: 0.6721 – val_loss: 2.6481 – 47ms/epoch – 47ms/step Epoch 83/100 1/1 – 0s – loss: 0.6435 – val_loss: 2.6652 – 43ms/epoch – 43ms/step Epoch 84/100 1/1 – 0s – loss: 0.6161 – val_loss: 2.6820 – 40ms/epoch – 40ms/step Epoch 85/100 1/1 – 0s – loss: 0.5898 – val_loss: 2.6984 – 42ms/epoch – 42ms/step Epoch 86/100 1/1 – 0s – loss: 0.5647 – val_loss: 2.7145 – 39ms/epoch – 39ms/step Epoch 87/100 1/1 – 0s – loss: 0.5408 – val_loss: 2.7302 – 36ms/epoch – 36ms/step Epoch 88/100 1/1 – 0s – loss: 0.5182 – val_loss: 2.7456 – 40ms/epoch – 40ms/step Epoch 89/100 1/1 – 0s – loss: 0.4968 – val_loss: 2.7606 – 42ms/epoch – 42ms/step Epoch 90/100 1/1 – 0s – loss: 0.4765 – val_loss: 2.7752 – 38ms/epoch – 38ms/step Epoch 91/100 1/1 – 0s – loss: 0.4574 – val_loss: 2.7895 – 42ms/epoch – 42ms/step Epoch 92/100 1/1 – 0s – loss: 0.4393 – val_loss: 2.8034 – 40ms/epoch – 40ms/step Epoch 93/100 1/1 – 0s – loss: 0.4222 – val_loss: 2.8169 – 43ms/epoch – 43ms/step Epoch 94/100 1/1 – 0s – loss: 0.4060 – val_loss: 2.8300 – 44ms/epoch – 44ms/step Epoch 95/100 1/1 – 0s – loss: 0.3907 – val_loss: 2.8427 – 55ms/epoch – 55ms/step Epoch 96/100 1/1 – 0s – loss: 0.3762 – val_loss: 2.8551 – 50ms/epoch – 50ms/step Epoch 97/100 1/1 – 0s – loss: 0.3623 – val_loss: 2.8671 – 50ms/epoch – 50ms/step Epoch 98/100 1/1 – 0s – loss: 0.3490 – val_loss: 2.8787 – 67ms/epoch – 67ms/step Epoch 99/100 1/1 – 0s – loss: 0.3363 – val_loss: 2.8899 – 65ms/epoch – 65ms/step Epoch 100/100 1/1 – 0s – loss: 0.3240 – val_loss: 2.9007 – 51ms/epoch – 51ms/step ————————————————————————— AttributeError Traceback (most recent call last) Cell In[3], line 57 54 return seed_text 56 # 製品特徴の自動生成（サンプルの開始テキスト: “コンパクトなデザイン”） —> 57 generated_description = generate_product_description(“コンパクトなデザイン”) 58 print(“Generated Product Description:”, generated_description) Cell In[3], line 51, in generate_product_description(seed_text, length) 49 sequence = tokenizer.texts_to_sequences([seed_text])[0] 50 padded_sequence = pad_sequences([sequence], maxlen=maxlen, padding=’pre’) —> 51 predicted_index = model.predict_classes(padded_sequence, verbose=0) 52 next_word = tokenizer.index_word.get(predicted_index[0], ”) 53 seed_text += ‘ ‘ + next_word AttributeError: ‘Sequential’ object has no attribute ‘predict_classes’
エラーメッセージによると、Sequentialオブジェクトにpredict_classes属性がないと言われています。このエラーは、TensorFlow 2.xでSequentialモデルからpredict_classesを使うことができないことを示しています。代わりに、predictメソッドを使用して予測を行う必要があります。
修正するために、predictメソッドを使用して次の単語の予測を行います。具体的には、各クラスの確率分布が返され、その中から最大の確率を持つインデックスを取得します。以下に修正されたgenerate_product_description関数を示します。
python
# 製品特徴の自動生成 def generate_product_description(seed_text, length=10): for _ in range(length): sequence = tokenizer.texts_to_sequences([seed_text])[0] padded_sequence = pad_sequences([sequence], maxlen=maxlen, padding='pre') predicted_probabilities = model.predict(padded_sequence)[0] predicted_index = np.argmax(predicted_probabilities) next_word = tokenizer.index_word.get(predicted_index, '') seed_text += ' ' + next_word return seed_text # 製品特徴の自動生成（サンプルの開始テキスト: "コンパクトなデザイン"） generated_description = generate_product_description("コンパクトなデザイン") print("Generated Product Description:", generated_description)
この修正後のコードでは、predictメソッドを使用して予測を行い、その結果から最大の確率を持つインデックスを取得して次の単語を見つけます。