Chest X-Ray Test

import numpy as np import pandas as pd import os import tensorflow as tf from tensorflow import keras from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Activation, Dense, Flatten, BatchNormalization, Conv2D, MaxPool2D, Dropout from tensorflow.keras.optimizers import Adam from tensorflow.keras.metrics import categorical_crossentropy from tensorflow.keras.preprocessing.image import ImageDataGenerator from sklearn.metrics import confusion_matrix import itertools import shutil import random import glob import matplotlib.pyplot as plt import warnings warnings.simplefilter(action='ignore', category=FutureWarning) %matplotlib inline

!ls /datasets/chestxray

dir_path ='/datasets/chestxray/input/chest-xray-pneumonia/chest_xray/train' test_path = '/datasets/chestxray/input/chest-xray-pneumonia/chest_xray/test' valid_path = '/datasets/chestxray/input/chest-xray-pneumonia/chest_xray/val'

IMAGE_SIZE= (224, 224) BATCH_SIZE = 32 train_dir = tf.keras.preprocessing.image_dataset_from_directory(dir_path, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, label_mode='binary') val_dir = tf.keras.preprocessing.image_dataset_from_directory(valid_path, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, label_mode='binary') test_dir = tf.keras.preprocessing.image_dataset_from_directory(test_path, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, label_mode='binary')

class_names = train_dir.class_names for images, labels in train_dir.take(1): images = images[0]/255.

train_ds = train_dir.prefetch(tf.data.AUTOTUNE) val_ds = val_dir.prefetch(tf.data.AUTOTUNE) test_ds = test_dir.prefetch(tf.data.AUTOTUNE)

train_ds

# Early Stopping Callbacks early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=3)

# Baseline model model_0 = Sequential([ Conv2D(16, 3, activation='relu', input_shape=(224,224,3)), MaxPool2D(), Dropout(0.5), Conv2D(16, 3, activation='relu'), MaxPool2D(), Dropout(0.5), Flatten(), Dense(1, activation='sigmoid') ]) # Compile the model model_0.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # Get the summary model_0.summary()

# fit the model history_model_0 = model_0.fit(train_ds, epochs=5, validation_data=val_ds, callbacks=[early_stopping])

# Plot the loss curves def plot_loss_curves(history): acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(1, len(acc) + 1) plt.plot(epochs, acc, label='Training acc') plt.plot(epochs, val_acc, label='Validation acc') plt.title('Training and validation accuracy') plt.legend() plt.figure() plt.plot(epochs, loss, label='Training loss') plt.plot(epochs, val_loss, label='Validation loss') plt.title('Training and validation loss') plt.legend() plt.show()

plot_loss_curves(history_model_0)