Hello,
Happy new year! I wish you a happy, productive, and fruitful new year.
I just have a Keras question and through you might have an idea on it,
especially that I have been trying to solve the issue for a while now but
couldn't yet solve the issue.
The following code always predicts the same class. I tried balancing the
training dataset and used different data (i.e. melanoma vs. nevus, cats vs.
dogs), but always having only "1" class predicted.
from keras import layers
> from keras import models
> from keras import optimizers
> from keras.preprocessing.image import ImageDataGenerator
> from sklearn.metrics import roc_curve
> from sklearn.metrics import auc
> import cv2
> import numpy as np
> import os
>
> train_directory = '/train'
> validation_directory = '/valid'
> test_directory = '/test'
> results_directory = '/results'
> correct_classification = 0
> number_of_test_images = 0
> labels = []
> prediction_probabilities = []
>
> model = models.Sequential()
>
>
> model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(512,512,3)))
> model.add(layers.MaxPooling2D(2,2))
> model.add(layers.Conv2D(64,(3,3),activation='relu'))
> model.add(layers.MaxPooling2D(2,2))
> model.add(layers.Conv2D(128,(3,3),activation='relu'))
> model.add(layers.MaxPooling2D(2,2))
> model.add(layers.Conv2D(256,(3,3),activation='relu'))
> model.add(layers.MaxPooling2D(2,2))
> model.add(layers.Conv2D(512,(3,3),activation='relu'))
> model.add(layers.MaxPooling2D(2,2))
>
> model.add(layers.Flatten())
> model.add(layers.Dense(1024,activation='relu'))
> model.add(layers.Dense(1,activation='sigmoid'))
>
>
> model.compile(loss='binary_crossentropy',optimizer='rmsprop',metrics=['acc'])
>
> train_data = ImageDataGenerator(rescale=1.0/255)
> validation_data = ImageDataGenerator(rescale=1.0/255)
>
> train_generator =
> train_data.flow_from_directory(train_directory,target_size=(512,512),batch_size=20,class_mode='binary')
> validation_generator =
> validation_data.flow_from_directory(validation_directory,target_size=(512,512),batch_size=20,class_mode='binary')
>
> history = model.fit_generator(train_generator,
> steps_per_epoch=10,
> epochs=10,
> validation_data=validation_generator,
> validation_steps=5)
>
> model.save('my_model.h5')
>
> for root, dirs, files in os.walk(test_directory):
> for file in files:
> img = cv2.imread(root + '/' + file)
> img = cv2.resize(img,(512,512),interpolation=cv2.INTER_AREA)
> img = np.expand_dims(img, axis=0)
> img = img/255.0
> if os.path.basename(root) == 'nevus':
> label = 1
> elif os.path.basename(root) == 'melanoma':
> label = 0
> labels.append(label)
> img_class = model.predict_classes(img)
> img_class_probability = model.predict(img)
> prediction_probability = img_class_probability[0]
> prediction_probabilities.append(prediction_probability)
> prediction = img_class[0]
> if prediction == label:
> correct_classification = correct_classification + 1
Any ideas on how the issue could be solved?
Thanks a lot, and appreciate you kind support.
