Merge pull request #363 from sukkritsharmaofficial/master

Added plots in convolutional_query!

.gitignore

@@ -130,4 +130,5 @@ venv.bak/
 dmypy.json
 
 # Pyre type checker
-.pyre/
+.pyre/
+.DS_Store

libra/plotting/generate_plots.py

@@ -140,7 +140,7 @@ def generate_regression_plots(history, data, label):
     return plots, plot_names
 
 
-def generate_classification_plots(history, data, label, model, X_test, y_test):
+def generate_classification_plots(history):
     '''
     plotting function that generates classification plots
 

libra/queries.py

@@ -760,7 +760,8 @@ def convolutional_query(self,
                             width=None,
                             show_feature_map=False,
                             save_as_tfjs=None,
-                            save_as_tflite=None):
+                            save_as_tflite=None,
+                            generate_plots=None):
         '''
         Calls the body of the convolutional neural network query which is located in the feedforward.py file
         :param instruction: The objective that you want to model (str).
@@ -798,7 +799,8 @@ def convolutional_query(self,
             height=height,
             width=width,
             save_as_tfjs=save_as_tfjs,
-            save_as_tflite=save_as_tflite)
+            save_as_tflite=save_as_tflite,
+            generate_plots=generate_plots)
 
         if show_feature_map:
             model = self.models["convolutional_NN"]["model"]

libra/query/feedforward_nn.py

@@ -458,7 +458,7 @@ def classification_ann(instruction,
     plots = {}
     if generate_plots:
         plots = generate_classification_plots(
-            models[len(models) - 1], data, y, model, X_test, y_test)
+            models[len(models) - 1])
 
     if save_model:
         save(final_model, save_model, save_path)
@@ -508,7 +508,8 @@ def convolutional(instruction=None,
                   height=None,
                   width=None,
                   save_as_tfjs=None,
-                  save_as_tflite=None):
+                  save_as_tflite=None,
+                  generate_plots=True):
     '''
     Body of the convolutional function used that is called in the neural network query
     if the data is presented in images.
@@ -787,6 +788,26 @@ def convolutional(instruction=None,
         epochs=epochs,
         verbose=verbose)
 
+    models = []
+    losses = []
+    accuracies = []
+    model_data = []
+
+    model_data.append(model)
+    models.append(history)
+
+    losses.append(history.history["val_loss"]
+                      [len(history.history["val_loss"]) - 1])
+    accuracies.append(history.history['val_accuracy']
+                      [len(history.history['val_accuracy']) - 1])
+
+    # final_model = model_data[accuracies.index(max(accuracies))]
+    # final_hist = models[accuracies.index(max(accuracies))]
+
+    plots = {}
+    if generate_plots:
+        plots = generate_classification_plots(models[len(models) - 1])
+
     logger('->',
            'Final training accuracy: {}'.format(history.history['accuracy'][len(history.history['accuracy']) - 1]))
     logger('->', 'Final validation accuracy: {}'.format(
@@ -816,6 +837,7 @@ def convolutional(instruction=None,
         'data': {'train': X_train, 'test': X_test},
         'shape': input_shape,
         "model": model,
+        "plots": plots,
         'losses': {
             'training_loss': history.history['loss'],
             'val_loss': history.history['val_loss']},

libra/query/nlp_queries.py

@@ -207,8 +207,7 @@ def text_classification_query(self, instruction, drop=None,
         # generates appropriate classification plots by feeding all
         # information
         logger("Generating plots")
-        plots = generate_classification_plots(
-            history, X, Y, model, X_test, y_test)
+        plots = generate_classification_plots(history)
 
     if save_model:
         save(model, save_model, save_path=save_path)

libra/query/supplementaries.py

@@ -177,8 +177,7 @@ def tune_helper(
         logger("->", 'Best Hyperparameters Found: {}'.format(returned_pms.values))
         if generate_plots:
             logger("Generating updated plots")
-            plots = generate_classification_plots(
-                    history, data, target_column, returned_model, X_test, y_test)
+            plots = generate_classification_plots(history)
 
 
         logger("Re-stored model under 'classification_ANN' key")