Accuracy Stuck at 50% Keras

Question

Code

import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential,Model
from keras.layers import Dropout, Flatten, Dense,Input
from keras import applications
from keras.preprocessing import image
from keras import backend as K
K.set_image_dim_ordering('tf')


# dimensions of our images.
img_width, img_height = 150,150

top_model_weights_path = 'bottleneck_fc_model.h5'
train_data_dir = 'Cats and Dogs Dataset/train'
validation_data_dir = 'Cats and Dogs Dataset/validation'
nb_train_samples = 20000
nb_validation_samples = 5000
epochs = 50
batch_size = 16
input_tensor = Input(shape=(150,150,3))

base_model=applications.VGG16(include_top=False, weights='imagenet',input_tensor=input_tensor)
for layer in base_model.layers:
    layer.trainable = False

top_model=Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(256,activation="relu"))
top_model.add(Dropout(0.5))
top_model.add(Dense(1,activation='softmax'))
top_model.load_weights(top_model_weights_path)
model = Model(inputs=base_model.input,outputs=top_model(base_model.output))


datagen = ImageDataGenerator(rescale=1. / 255)

train_data = datagen.flow_from_directory(train_data_dir,target_size=(img_width, img_height),batch_size=batch_size,classes=['dogs', 'cats'],class_mode="binary",shuffle=False)


validation_data = datagen.flow_from_directory(validation_data_dir,target_size=(img_width, img_height),classes=['dogs', 'cats'], batch_size=batch_size,class_mode="binary",shuffle=False)


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

model.fit_generator(train_data, steps_per_epoch=nb_train_samples//batch_size, epochs=epochs,validation_data=validation_data, shuffle=False,verbose=

I have implemented a Image Classifier on the cats and dogs Dataset(https://www.kaggle.com/c/dogs-vs-cats/data) using keras(transfer learned using the VGG16 network). The code runs without errors but the accuracy is stuck at 0.0 % for about half of the epoch and after half it increases to an of accuracy of 50%. I am using Atom with hydrogen.

How do I fix this.I really don't think I have a bias problem with such a dataset with VGG16(although i am relatively new to this field).

Answer 1

Change your activation at your output layer to sigmoid

from

top_model.add(Dense(1,activation='softmax')) 

to

top_model.add(Dense(1,activation='sigmoid'))