Add total params to cifar

Author: vanpelt

examples/keras-cifar/cifar-cnn.py

@@ -37,6 +37,9 @@
 model.compile(loss='categorical_crossentropy',
               optimizer=tf.keras.optimizers.Adam(config.learn_rate),
               metrics=['accuracy'])
+# log the number of total parameters
+config.total_params = model.count_params()
+print("Total params: ", config.total_params)
 
 model.fit(X_train, y_train, epochs=10, batch_size=128, validation_data=(X_test, y_test),
           callbacks=[wandb.keras.WandbCallback(data_type="image", labels=class_names, save_model=False)])

examples/keras-cifar/cifar-gen.py

@@ -40,7 +40,9 @@
 model.compile(loss='categorical_crossentropy',
               optimizer="adam",
               metrics=['accuracy'])
-
+# log the number of total parameters
+config.total_params = model.count_params()
+print("Total params: ", config.total_params)
 X_train = X_train.astype('float32') / 255.
 X_test = X_test.astype('float32') / 255.
 

examples/keras-cifar/cifar-transfer.py

@@ -45,6 +45,8 @@
 model.compile(loss='categorical_crossentropy',
               optimizer=Adam(config.learn_rate),
               metrics=['accuracy'])
-
+# log the number of total parameters
+config.total_params = model.count_params()
+print("Total params: ", config.total_params)
 model.fit(X_train, y_train, epochs=10, batch_size=128, validation_data=(X_test, y_test),
           callbacks=[WandbCallback(data_type="image", labels=class_names, save_model=False)])

examples/keras-cifar/cifar.py

@@ -24,6 +24,8 @@
 model.compile(loss='mse',
               optimizer=tf.keras.optimizers.Adam(config.learn_rate),
               metrics=['accuracy'])
-
+# log the number of total parameters
+config.total_params = model.count_params()
+print("Total params: ", config.total_params)
 model.fit(X_train, y_train, epochs=10, batch_size=128, validation_data=(X_test, y_test),
           callbacks=[wandb.keras.WandbCallback(data_type="image", labels=class_names, save_model=False)])