# Keras Classifier returns similar output for all Predictions

I completed training the model with an accuracy of 1.000 and a validation accuracy of 0.9565. Unfortunately whenever i input a image into my model i get the same output regardless. Am i doing something wrong when predicting or during my training. W and A are my class labels.

My folder structure for the image generators are as follows:

images/

a/ a001.jpg.png.. w/ w002.jpg.png..

train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2)

test_datagen = ImageDataGenerator(rescale=1./255)

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense

model = Sequential()

model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors

model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])

batch_size = 64

# this is a generator that will read pictures found in
# subfolers of 'data/train', and indefinitely generate
# batches of augmented image data
train_generator = train_datagen.flow_from_directory(
'C:\\Users\\Zahid\\Desktop\\Dataset\\train',  # this is the target directory
target_size=(150, 150),  # all images will be resized to 150x150
batch_size=batch_size,
color_mode='rgb',
class_mode='binary')  # since we use binary_crossentropy loss, we need binary labels

# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
'C:\\Users\\Zahid\\Desktop\\Dataset\\val',
target_size=(150, 150),
batch_size=batch_size,
color_mode='rgb',
class_mode='binary')

model.fit_generator(
train_generator,
steps_per_epoch=2000 // batch_size,
epochs=50,
validation_data=validation_generator,
validation_steps=800 // batch_size)
model.save_weights('first_try.h5')

resized_image = cv2.resize(image, (150, 150))
x = img_to_array(resized_image)
x = x.reshape((1,) + x.shape)
x = x/255
print(x.shape)
scores_train = model.predict(x)
print(scores_train)

• what is number 1 in your last dense layer? Do you have only one class? and why you use a sigmoid function in the output layer? try using softmax function, it is better. – Hunar Apr 6 '19 at 13:40
• @honas.cs I Have two classes as mentioned in the question , and i followed a keras example to train this model. As shown in my folder structure i have seperated the classes into two seperate folders and trained them. – Zahid Ahmed Apr 6 '19 at 13:43