from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import tensorflow as tf
from PIL import Image
import numpy as np
import itertools
import os
im_height = 512
im_width = 512
batch_size = 32
epochs = 10
image_path = "./input/chest-xray-pneumonia/chest_xray/"
train_dir = image_path + "train"
validation_dir = image_path + "test"
test_dir = image_path + "valid"
train_image_generator = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
validation_image_generator = ImageDataGenerator(rescale=1. / 255)
test_image_generator = ImageDataGenerator(rescale=1. / 255)
train_data_gen = train_image_generator.flow_from_directory(directory=train_dir,
batch_size=batch_size,
shuffle=True,
target_size=(im_height, im_width),
class_mode='categorical')
total_train = train_data_gen.n
val_data_gen = validation_image_generator.flow_from_directory(directory=validation_dir,
batch_size=batch_size,
shuffle=False,
target_size=(im_height, im_width),
class_mode='categorical')
total_val = val_data_gen.n
test_data_gen = test_image_generator.flow_from_directory(directory=test_dir,
batch_size=batch_size,
shuffle=False,
target_size=(im_height, im_width),
class_mode='categorical')
total_test = test_data_gen.n
covn_base = tf.keras.applications.DenseNet201(weights='imagenet', include_top = False,input_shape=(im_height,im_width,3))
covn_base.trainable = False
model = tf.keras.Sequential()
model.add(covn_base)
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dropout(rate=0.2))
model.add(tf.keras.layers.Dense(2, activation='softmax'))
model.summary()
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
loss = 'categorical_crossentropy',
metrics=['accuracy']
)
reduce_lr = ReduceLROnPlateau(
monitor='val_loss',
factor=0.1,
patience=2,
mode='auto',
verbose=1
)
checkpoint = ModelCheckpoint(
filepath='DenseNet201.ckpt',
monitor='val_accuracy',
save_weights_only=False,
save_best_only=True,
mode='auto',
period=1
)
history = model.fit(x=train_data_gen,
steps_per_epoch=total_train // batch_size,
epochs=epochs,
validation_data=val_data_gen,
validation_steps=total_val // batch_size,
callbacks=[checkpoint, reduce_lr])