diff --git a/labworks/LW4/readme.md b/labworks/LW4/readme.md
index ccfb8e5..4011707 100644
--- a/labworks/LW4/readme.md
+++ b/labworks/LW4/readme.md
@@ -184,6 +184,7 @@ model.add(layers.Dense(1, activation='sigmoid'))
 
 model.summary()
 ```
+```
 /usr/local/lib/python3.12/dist-packages/keras/src/layers/core/embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.
   warnings.warn(
 /usr/local/lib/python3.12/dist-packages/keras/src/layers/core/embedding.py:100: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
@@ -203,6 +204,7 @@ Model: "sequential"
  Total params: 184,897 (722.25 KB)
  Trainable params: 184,897 (722.25 KB)
  Non-trainable params: 0 (0.00 B)
+ ```
  ```python
  # компилируем и обучаем модель
 batch_size = 64
@@ -242,6 +244,7 @@ y_pred = [1 if y_score[i,0]>=0.5 else 0 for i in range(len(y_score))]
 from sklearn.metrics import classification_report
 print(classification_report(y_test, y_pred, labels = [0, 1], target_names=['Negative', 'Positive']))
 ```
+```
 782/782 ━━━━━━━━━━━━━━━━━━━━ 8s 10ms/step
               precision    recall  f1-score   support
 
@@ -251,6 +254,7 @@ print(classification_report(y_test, y_pred, labels = [0, 1], target_names=['Nega
     accuracy                           0.86     25000
    macro avg       0.86      0.86      0.86     25000
 weighted avg       0.86      0.86      0.86     25000
+```
 ```python
 #построение ROC-кривой и AUC ROC
 from sklearn.metrics import roc_curve, auc