typo fix

maxmekiska · Nov 1, 2022 · 3035da1 · 3035da1
1 parent 2f85edb
commit 3035da1
Showing 1 changed file with 4 additions and 4 deletions.
diff --git a/README.md b/README.md
@@ -191,7 +191,7 @@ predictor.create_encdec_gru(optimizer: str = 'adam', loss: str = 'mean_squared_e
 
 # Fit the predictor object - more callback settings at:
 
-https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
+# https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
 
 predictor.fit_model(epochs: int, show_progress: int = 1, validation_split: float = 0.20,
                     batch_size: int = 10, monitor='loss', patience=3)
@@ -251,7 +251,7 @@ predictor.create_cnnbigru(optimizer: str = 'adam', loss: str = 'mean_squared_err
 
 # Fit the predictor object - more callback settings at:
 
-https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
+# https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
 
 predictor.fit_model(epochs: int, show_progress: int = 1, validation_split: float = 0.20,
                     batch_size: int = 10, monitor='loss', patience=3)
@@ -342,7 +342,7 @@ predictor.create_encdec_gru(optimizer: str = 'adam', loss: str = 'mean_squared_e
 
 # Fit the predictor object - more callback settings at:
 
-https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
+# https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
 
 predictor.fit_model(epochs: int, show_progress: int = 1, validation_split: float = 0.20,
                     batch_size: int = 10, monitor='loss', patience=3)
@@ -402,7 +402,7 @@ predictor.create_cnnbigru(optimizer: str = 'adam', loss: str = 'mean_squared_err
 
 # Fit the predictor object - more callback settings at:
 
-https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
+# https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
 
 predictor.fit_model(epochs: int, show_progress: int = 1, validation_split: float = 0.20,
                     batch_size: int = 10, monitor='loss', patience=3)