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train.py
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train.py
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from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import img_to_array
from keras.utils import to_categorical, plot_model
from keras.models import Sequential
from keras.layers import BatchNormalization, Conv2D, MaxPooling2D, Activation, Flatten, Dropout, Dense
from keras import backend as K
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import random
import cv2
import os
import glob
# initial parameters
epochs = 100
lr = 1e-3
batch_size = 64
img_dims = (96,96,3)
data = []
labels = []
# load image files from the dataset
image_files = [f for f in glob.glob(r'C:\Files\gender_dataset_face' + "/**/*", recursive=True) if not os.path.isdir(f)]
random.shuffle(image_files)
# converting images to arrays and labelling the categories
for img in image_files:
image = cv2.imread(img)
image = cv2.resize(image, (img_dims[0],img_dims[1]))
image = img_to_array(image)
data.append(image)
label = img.split(os.path.sep)[-2] # C:\Files\gender_dataset_face\woman\face_1162.jpg
if label == "woman":
label = 1
else:
label = 0
labels.append([label]) # [[1], [0], [0], ...]
# pre-processing
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
# split dataset for training and validation
(trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.2,
random_state=42)
trainY = to_categorical(trainY, num_classes=2) # [[1, 0], [0, 1], [0, 1], ...]
testY = to_categorical(testY, num_classes=2)
# augmenting datset
aug = ImageDataGenerator(rotation_range=25, width_shift_range=0.1,
height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
horizontal_flip=True, fill_mode="nearest")
# define model
def build(width, height, depth, classes):
model = Sequential()
inputShape = (height, width, depth)
chanDim = -1
if K.image_data_format() == "channels_first": #Returns a string, either 'channels_first' or 'channels_last'
inputShape = (depth, height, width)
chanDim = 1
# The axis that should be normalized, after a Conv2D layer with data_format="channels_first",
# set axis=1 in BatchNormalization.
model.add(Conv2D(32, (3,3), padding="same", input_shape=inputShape))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(3,3)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3,3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(Conv2D(64, (3,3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3,3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(Conv2D(128, (3,3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation("relu"))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(classes))
model.add(Activation("sigmoid"))
return model
# build model
model = build(width=img_dims[0], height=img_dims[1], depth=img_dims[2],
classes=2)
# compile the model
opt = Adam(lr=lr, decay=lr/epochs)
model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])
# train the model
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=batch_size),
validation_data=(testX,testY),
steps_per_epoch=len(trainX) // batch_size,
epochs=epochs, verbose=1)
# save the model to disk
model.save('gender_detection.model')
# plot training/validation loss/accuracy
plt.style.use("ggplot")
plt.figure()
N = epochs
plt.plot(np.arange(0,N), H.history["loss"], label="train_loss")
plt.plot(np.arange(0,N), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0,N), H.history["acc"], label="train_acc")
plt.plot(np.arange(0,N), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="upper right")
# save plot to disk
plt.savefig('plot.png')