Add details about parameter counts

Author: bpesquet

pytorch_tutorial/linear_regression/README.md

@@ -118,6 +118,7 @@ print(model)
 # Compute and print parameter count
 n_params = get_parameter_count(model)
 print(f"Model has {n_params} trainable parameters")
+# Linear layers have (in_features + 1) * out_features parameters
 assert n_params == 2
 ```
 

pytorch_tutorial/linear_regression/test_linear_regression.py

@@ -86,6 +86,7 @@ def test_linear_regression(show_plots=False):
     # Compute and print parameter count
     n_params = get_parameter_count(model)
     print(f"Model has {n_params} trainable parameters")
+    # Linear layers have (in_features + 1) * out_features parameters
     assert n_params == 2
 
     # Use Mean Squared Error loss

pytorch_tutorial/logistic_regression/README.md

@@ -129,7 +129,7 @@ print(model)
 # Compute and print parameter count
 n_params = get_parameter_count(model)
 print(f"Model has {n_params} trainable parameters")
-# Number of entries is 2 (x- and y-coordinates) + 1 (bias)
+# Linear layers have (in_features + 1) * out_features parameters
 assert n_params == 3 * output_dim
 ```
 

pytorch_tutorial/logistic_regression/test_logistic_regression.py

@@ -68,7 +68,7 @@ def test_logistic_regression(show_plots=False):
     # Compute and print parameter count
     n_params = get_parameter_count(model)
     print(f"Model has {n_params} trainable parameters")
-    # Number of entries is 2 (x- and y-coordinates) + 1 (bias)
+    # Linear layers have (in_features + 1) * out_features parameters
     assert n_params == 3 * output_dim
 
     # Use cross-entropy loss function.

pytorch_tutorial/multilayer_perceptron/README.md

@@ -139,6 +139,7 @@ print(model)
 # Compute and print parameter count
 n_params = get_parameter_count(model)
 print(f"Model has {n_params} trainable parameters")
+# Linear layers have (in_features + 1) * out_features parameters.
 # Hidden layer has (2 + 1) * hidden_layer_dim parameters.
 # Output layer has (hidden_layer_dim + 1) * 1 parameters
 assert n_params == 3 * hidden_layer_dim + hidden_layer_dim + 1

pytorch_tutorial/multilayer_perceptron/test_multilayer_perceptron.py

@@ -78,6 +78,7 @@ def test_multilayer_perceptron(show_plots=False):
     # Compute and print parameter count
     n_params = get_parameter_count(model)
     print(f"Model has {n_params} trainable parameters")
+    # Linear layers have (in_features + 1) * out_features parameters.
     # Hidden layer has (2 + 1) * hidden_layer_dim parameters.
     # Output layer has (hidden_layer_dim + 1) * 1 parameters
     assert n_params == 3 * hidden_layer_dim + hidden_layer_dim + 1