Keras: Overfitting Model?

Question

I am trying to create a binary image classification model using the malaria dataset from NIH (National Library of Medicine) which contains approximately 27,000 images of each class (infected/uninfected).

There seems to be overfitting and I have tried to play around with different batch sizes, steps per epoch/validation steps, using different hidden layers and adding callbacks etc. The graph always shows a straight line that is either dramatically increasing or decreasing, rather than increasing steadily with some decreases as it learns (which from my understanding, is how it should be). Below is an example, most turn out somewhat similar to this.

I'm new to deep learning, I have read a lot about overfitting and trying to find a solution. But I think there must be something I'm doing wrong and/or misunderstanding. If someone is able to spot something that doesn't look right and is able to point me in the right direction, it would be greatly appreciated!

from keras.layers import MaxPooling2D, Conv2D, Flatten, Dense, Dropout
from keras_preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from keras.models import Sequential
import matplotlib.pyplot as plt
import constants as c
import numpy as np
import keras

# Clear session and instantiate model
keras.backend.clear_session()
model = Sequential()

# Load images & labels
cells = np.load(c.cells_path)
labels = np.load(c.labels_path)

# Shuffle the entire dataset
n = np.arange(cells.shape[0])
np.random.shuffle(n)

# Update numpy files with shuffled data
cells = cells[n]
labels = labels[n]

# Split the dataset into train/validation/test
train_x, test_x, train_y, test_y = train_test_split(cells, labels, test_size=1 - c.train_ratio, shuffle=False)
val_x, test_x, val_y, test_y = train_test_split(test_x, test_y, test_size=c.test_ratio / (c.test_ratio + c.val_ratio),
                                                shuffle=False)

# The amount of images in each set
print('Training data shape: ', train_x.shape)
print('Validation data shape: ', val_x.shape)
print('Testing data shape: ', test_x.shape)

# Neural network
model.add(Conv2D(32, (3, 3), input_shape=c.input_shape, activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(units=64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(units=1, activation='sigmoid'))

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

# Data augmentation
train_datagen = ImageDataGenerator(rescale=1. / 255,
                                   rotation_range=20,
                                   width_shift_range=0.05,
                                   height_shift_range=0.05,
                                   shear_range=0.05,
                                   zoom_range=0.05,
                                   horizontal_flip=True,
                                   fill_mode='nearest')

validation_datagen = ImageDataGenerator(rescale=1. / 255)
testing_datagen = ImageDataGenerator(rescale=1. / 255)

training_dataset = train_datagen.flow(train_x, train_y, batch_size=32)
validation_dataset = validation_datagen.flow(val_x, val_y, batch_size=32)
testing_dataset = validation_datagen.flow(val_x, val_y, batch_size=32)

# Add callbacks to prevent overfitting
es = EarlyStopping(monitor='accuracy',
                   min_delta=0,
                   patience=2,
                   verbose=0,
                   mode='max')

rlrop = ReduceLROnPlateau(monitor='val_loss',
                          factor=0.2,
                          patience=0.5,
                          min_lr=0.001)

checkpoint = ModelCheckpoint("Model.h5")

# Perform backpropagation and update weights in model
history = model.fit_generator(training_dataset,
                              epochs=50,
                              validation_data=validation_dataset,
                              callbacks=[es, checkpoint, rlrop])

# Save model & weights
model.save_weights("Model_weights.h5")
model.save("Model.h5")

# Plot accuracy graph
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()

Keras: Overfitting Model?

Answers (1)

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