I am writing a code to classify images from two classes, dogs and cats. I wrote the below code, but always all the dogs images are classified as cats as shown in the confusion matrix.
Am I missing something in the code?
Note: I tried epochs up to 70, but got same results.
from keras.models import Sequential
from keras.layers import Conv2D,Activation,MaxPooling2D,Dense,Flatten,Dropout
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from IPython.display import display
import matplotlib.pyplot as plt
from PIL import Image
from sklearn.metrics import classification_report, confusion_matrix
classifier = Sequential()
classifier.add(Conv2D(32,(3,3),input_shape=(64,64,3)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size =(2,2)))
classifier.add(Conv2D(32,(3,3)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size =(2,2)))
classifier.add(Conv2D(64,(3,3)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size =(2,2)))
classifier.add(Flatten())
classifier.add(Dense(64))
classifier.add(Activation('relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(1))
classifier.add(Activation('sigmoid'))
classifier.summary()
classifier.compile(optimizer ='rmsprop',
loss ='binary_crossentropy',
metrics =['accuracy'])
train_datagen = ImageDataGenerator(rescale =1./255,
shear_range =0.2,
zoom_range = 0.2,
horizontal_flip =True)
test_datagen = ImageDataGenerator(rescale = 1./255)
batchsize=32
training_set = train_datagen.flow_from_directory('/home/osboxes/Downloads/Downloads/dogs-vs-cats/train/',
target_size=(64,64),
batch_size= batchsize,
class_mode='binary')
test_set = test_datagen.flow_from_directory('/home/osboxes/Downloads/Downloads/dogs-vs-cats/test/',
target_size = (64,64),
batch_size = batchsize,
shuffle=False,
class_mode ='binary')
history=classifier.fit_generator(training_set,
steps_per_epoch =9000 // batchsize,
epochs = 30,
validation_data =test_set,
validation_steps = 4500 // batchsize)
Y_pred = classifier.predict_generator(test_set, steps=4500 // batchsize+1)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(test_set.classes, y_pred))
print('Classification Report')
target_names = test_set.classes
class_labels = list(test_set.class_indices.keys())
report = classification_report(target_names, y_pred, target_names=class_labels)
print(report)
# 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()