CODEWITHSUNDEEP
pythonmachine-learningkerasconv-neural-networkpre-trained-model
Niteya Shah
Niteya Shah

Reputation: 1824

Accuracy Stuck at 50% Keras

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.

My directory

Results of execution

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).

Upvotes: 5

Views: 15263

Answers (2)

Abhishek Srivastava
Abhishek Srivastava

Reputation: 46

  • Changing the activation function is a good step, along with this pay attention to the size of the dataset you are using.
  • If small then reduce batch size, reducing batch size solved it for me :)

I guess my test set was too small to feed large batches into the CNN.

Upvotes: 0

Ioannis Nasios
Ioannis Nasios

Reputation: 8527

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'))

Upvotes: 8

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