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buildModel.py
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import keras
import tensorflow as tf
from keras import optimizers
from keras.backend import image_data_format
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import to_categorical
from keras.preprocessing import image
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from keras.callbacks import EarlyStopping
from tensorflow.keras.optimizers import RMSprop
from keras.optimizers import SGD
from keras.callbacks import CSVLogger
from keras.applications.mobilenet import MobileNet
from keras.layers import GlobalAveragePooling2D, Dense, Dropout, Flatten
from keras.models import Sequential
from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, Activation, concatenate
"""
Data parameters
Image Parameters
Batch parameters
Root directories for data
"""
labels = ['Atelectasis', 'Consolidation', 'Infiltration',
'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion',
'Pneumonia', 'Pleural_Thickening', 'Cardiomegaly', 'Nodule', 'Mass',
'Hernia']
image_width = 256
image_height = 256
batch_size = 32
epochs = 50
nb_train_samples = len(pd.read_csv(r'C:\Users\Alejandro\Documents\GitHub\MachineLearningModels\trainCsv.csv'))
nb_validation_samples = len(pd.read_csv(r'C:\Users\Alejandro\Documents\GitHub\MachineLearningModels\validationCsv.csv'))
print(nb_train_samples, nb_validation_samples)
dataframe_train = pd.read_csv(r'C:\Users\Alejandro\Documents\GitHub\MachineLearningModels\trainCsv.csv')
dataframe_validation = pd.read_csv(r'C:\Users\Alejandro\Documents\GitHub\MachineLearningModels\validationCsv.csv')
dataframe_test = pd.read_csv(r'C:\Users\Alejandro\Documents\GitHub\MachineLearningModels\testCsv.csv')
print(dataframe_train.head())
print(dataframe_train.columns)
datagen = ImageDataGenerator(rescale=1. / 255.)
test_datagen = ImageDataGenerator(rescale=1. / 255.)
train_generator = datagen.flow_from_dataframe(
dataframe=dataframe_train,
directory="D:\DescargasChrome\input\images*\images",
x_col="FullPath",
y_col=labels,
batch_size=32,
seed=42,
shuffle=True,
class_mode="raw",
target_size=(256, 256))
valid_generator = test_datagen.flow_from_dataframe(
dataframe=dataframe_validation,
directory="D:\DescargasChrome\input\images*\images",
x_col="FullPath",
y_col=labels,
batch_size=32,
seed=42,
shuffle=True,
class_mode="raw",
target_size=(256, 256))
test_generator = test_datagen.flow_from_dataframe(
dataframe=dataframe_test,
directory="D:\DescargasChrome\input\images*\images",
x_col="FullPath",
y_col=labels,
batch_size=32,
seed=42,
shuffle=True,
class_mode="raw",
target_size=(256, 256))
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='Same', activation='relu', input_shape=(256,256,1)))
model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='Same'))
model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='Same'))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=128, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(Conv2D(filters=128, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=128, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(Conv2D(filters=128, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=256, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=256, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(MaxPool2D(pool_size=(2, 2)))
# dense block with activation function
model.add(Flatten())
model.add(Dense(128, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(len(labels), activation="sigmoid"))
model.summary()
# model = Sequential()
# model.add(Conv2D(filters=16, kernel_size=(5, 5), activation="relu", input_shape=(256, 256, 1)))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
# model.add(Conv2D(filters=32, kernel_size=(5, 5), activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
# model.add(Conv2D(filters=64, kernel_size=(5, 5), activation="relu"))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
# model.add(Conv2D(filters=64, kernel_size=(5, 5), activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
# model.add(Flatten())
# model.add(Dense(128, activation='relu'))
# model.add(Dropout(0.5))
# model.add(Dense(64, activation='relu'))
# model.add(Dropout(0.5))
# model.add(Dense(14, activation='sigmoid'))
# model.compile(optimizer='adam', loss='binary_crossentropy',
# metrics=['binary_accuracy', 'mae'])
# model.summary()
#Below the original model
# model = Sequential()
# model.add(Conv2D(32, (3, 3), padding='same',
# input_shape=(256,256,3))) #change to 448x448x1
# model.add(Activation('relu'))
# model.add(Conv2D(32, (3, 3)))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
# model.add(Conv2D(64, (3, 3), padding='same'))
# model.add(Activation('relu'))
# model.add(Conv2D(64, (3, 3)))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
# model.add(Flatten())
# model.add(Dense(128)) #512
# model.add(Activation('relu'))
# model.add(Dropout(0.5))
# model.add(Dense(14, activation='sigmoid'))
# model.summary()
# model.compile(optimizers.rmsprop(lr=0.0001, decay=1e-6),loss="binary_crossentropy",metrics=["accuracy"])
#
# STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size
# STEP_SIZE_VALID=valid_generator.n//valid_generator.batch_size
# STEP_SIZE_TEST=test_generator.n//test_generator.batch_size
#
#
# model.fit_generator(generator=train_generator,
# steps_per_epoch=STEP_SIZE_TRAIN,
# validation_data=valid_generator,
# validation_steps=STEP_SIZE_VALID,
# epochs=1
# )
# model.save('modelo2.h5')
# model_json= model.to_json()
# with open("model_num2.json", "w") as json_file:
# json_file.write(model_json)
# test_generator.reset()
# pred=model.predict_generator(test_generator,
# steps=STEP_SIZE_TEST,
# verbose=1)
# pred_bool = (pred >0.5)
#
# predictions = pred_bool.astype(int)
#
# #columns should be the same order of y_col
# results=pd.DataFrame(predictions, columns=labels)
# print(len(test_generator.filenames))
# results["Image Index"]=test_generator.filenames
# ordered_cols=["FullPath"]+labels
# results=results[ordered_cols]#To get the same column order
# results.to_csv("results.csv",index=False)
# training_directory = r'D:\DescargasChrome\data\train'
# training_datagen = ImageDataGenerator(rescale=1. / 255,
# shear_range=0.2,
# zoom_range=0.2,
# rotation_range=20,
# width_shift_range=0.2,
# height_shift_range=0.2,
# horizontal_flip=True)
#
# training_generator = training_datagen.flow_from_directory(training_directory,
# batch_size=batch_size,
# class_mode='categorical',
# target_size=(image_height, image_width))
#
# validation_directory = r'D:\DescargasChrome\data\validation'
# validation_datagen = ImageDataGenerator(rescale=1. / 255,
# shear_range=0.2,
# zoom_range=0.2,
# rotation_range=20,
# width_shift_range=0.2,
# height_shift_range=0.2,
# horizontal_flip=True)
#
# validation_generator = validation_datagen.flow_from_directory(validation_directory,
# batch_size=batch_size,
# class_mode='categorical',
# target_size=(image_height, image_width))
#
# if image_data_format() == 'channels_first':
# input_shape = (3, image_width, image_height) # 1 means B&W images if it was RGB it will be 3
# else:
# input_shape = (image_width, image_height, 3)
#
# def build_model(): firs try this should be useful for multiclass but is multilabel
# model = keras.models.Sequential()
#
# model = tf.keras.models.Sequential([
# tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(256, 256, 3)),
# tf.keras.layers.MaxPooling2D(2, 2),
# tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
# tf.keras.layers.MaxPooling2D(2, 2),
# tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
# tf.keras.layers.MaxPooling2D(2, 2),
# tf.keras.layers.Flatten(),
# tf.keras.layers.Dense(1024, activation='relu'),
# tf.keras.layers.Dense(14, activation='softmax') #change for sigmoid if multilabel
# ])
# model.compile(loss='categorical_crossentropy',
# optimizer=RMSprop(lr=0.001),
# metrics=['acc'])
#
# history = model.fit(
# training_generator,
# steps_per_epoch=36,
# epochs=100,
# validation_data=validation_generator,
# validation_steps=36
# )
#
# model.save_weights('first_try_weights.h5')
# model.save('first_try.h5')