update CNN model class with more diseases

skin_disease/convolutional_neural_network.py

 # importing libraries
-import keras, tensorflow
-import numpy as np
 
 from keras.preprocessing.image import ImageDataGenerator
 from keras.models import Sequential
 from keras.layers import Conv2D, MaxPooling2D
 from keras.layers import Activation, Dropout, Flatten, Dense
 from keras import backend as K
-
+import matplotlib.pyplot as plt
 
 img_width, img_height = 350, 350
 
@@ -39,6 +37,7 @@ model.add(MaxPooling2D(pool_size =(2, 2)))
 model.add(Flatten())
 model.add(Dense(64))
 model.add(Activation('relu'))
+
 model.add(Dropout(0.5))
 model.add(Dense(2))
 model.add(Activation('softmax'))
@@ -64,9 +63,27 @@ validation_generator = test_datagen.flow_from_directory(
     target_size =(img_width, img_height),
     batch_size = batch_size, class_mode ='sparse')
 
-model.fit_generator(train_generator,
+history = model.fit_generator(train_generator,
                     steps_per_epoch = nb_train_samples // batch_size,
                     epochs = epochs, validation_data = validation_generator,
                     validation_steps = nb_validation_samples // batch_size)
 
 model.save_weights('model_saved.h5')
+
+print(history.history)
+# summarize history for accuracy
+plt.plot(history.history['accuracy'])
+plt.plot(history.history['val_accuracy'])
+plt.title('model accuracy')
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()
+# summarize history for loss
+plt.plot(history.history['loss'])
+plt.plot(history.history['val_loss'])
+plt.title('model loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()

skin_disease/predict_image_class.py

@@ -9,9 +9,10 @@ from keras.layers import Dense, Dropout, Flatten
 from keras.models import Sequential
 print(tensorflow.__version__)
 print(keras.__version__)
+
 def predictImageClass(image):
     K.clear_session()
-    tensorflow.reset_default_graph()
+    # tensorflow.reset_default_graph()
 
     nb_train_samples = 112
     nb_validation_samples = 20
@@ -65,9 +66,12 @@ def predictImageClass(image):
     pred = modelI.predict(img)
     # pred = labels["label_names"][np.argmax(pred)]
     print(pred)
-    y = ["Ring worms", "Sarcoptic & demodectic mange"]
+    y = ["Sarcoptic & demodectic mange", "Ring worms"]
     print(np.argmax(pred))
     print(y[np.argmax(pred)])
-    return y[np.argmax(pred)]
+    return {
+        "pred": y[np.argmax(pred)],
+        "accuracy": str(pred[0][np.argmax(pred)])
+    }
 
-predictImageClass("data/test/Ringworm/Ringworm1.jpg")
\ No newline at end of file
+# predictImageClass("data/test/Ringworm/Ringworm1.jpg")