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NN.py
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NN.py
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# Imports
import tensorflow as tf
import keras
import os
import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, Dropout, Dense, Flatten, MaxPooling2D
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam
IMAGE_DIR = "Images"
DOG_BREED_IMAGE_BASE_DIR = os.path.join(IMAGE_DIR, "images")
DOG_BREED_IMAGE_DIR = os.path.join(DOG_BREED_IMAGE_BASE_DIR, "Images")
TEST_IMAGE_DIR = os.path.join("n02086910-papillon", "n02086910_21.jpg")
IMAGE_HEIGHT = 128
IMAGE_WIDTH = 128
BATCH_SIZE = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
DOG_BREED_IMAGE_DIR,
validation_split=0.2,
subset='training',
seed=325,
image_size=(IMAGE_WIDTH, IMAGE_HEIGHT),
batch_size=BATCH_SIZE,
)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
DOG_BREED_IMAGE_DIR,
subset="validation",
validation_split=0.2,
seed=352,
image_size=(IMAGE_WIDTH, IMAGE_HEIGHT),
batch_size=BATCH_SIZE,
)
test_ds = tf.keras.preprocessing.image_dataset_from_directory(
DOG_BREED_IMAGE_DIR,
seed=123,
image_size=(IMAGE_WIDTH, IMAGE_HEIGHT),
batch_size=BATCH_SIZE,
)
class_names = train_ds.class_names
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
train_ds = train_ds.cache().shuffle(10000)
val_ds = val_ds.cache()
normalization_layer = keras.layers.experimental.preprocessing.Rescaling(1. / 255)
normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
data_augmentation = Sequential([
keras.layers.experimental.preprocessing.RandomFlip("horizontal",
input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, 3)),
keras.layers.experimental.preprocessing.RandomRotation(0.1),
keras.layers.experimental.preprocessing.RandomZoom(0.1)
])
num_classes = 120
model = Sequential(name="Dog_Breed_Classifier", layers=[
data_augmentation,
keras.layers.experimental.preprocessing.Rescaling(1. / 255),
Conv2D(16, 3, padding='same', activation='relu'),
MaxPooling2D(),
Conv2D(32, 3, padding='same', activation='relu'),
MaxPooling2D(),
Conv2D(64, 3, padding='same', activation='relu'),
MaxPooling2D(),
Dropout(0.2),
Flatten(),
Dense(128, activation='relu'),
Dense(num_classes)])
model.compile(optimizer=Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
epochs = 5
checkpoint = ModelCheckpoint("Models/Dog_Classifier.h5", save_best_only=True)
def train_model():
model.load_weights("Models/Dog_Classifier.h5")
model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs,
callbacks=[checkpoint]
)
def predict_breed(image):
model.load_weights("Models/Dog_Classifier.h5")
img = keras.preprocessing.image.load_img(image,
target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))
image_array = keras.preprocessing.image.img_to_array(img)
image_array = tf.expand_dims(image_array, 0)
predictions = model.predict(image_array, verbose=True)
score = tf.nn.softmax(predictions[0])
print("This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score)))
return class_names[np.argmax(score)], 100 * np.max(score)
def evaluate_model():
model.load_weights("Models/Dog_Classifier.h5")
loss, acc = model.evaluate(test_ds, batch_size=32)
print("accuracy: {}%".format(100 * acc))
train_model()
# predict_breed("Images/images/Images/n02086910-papillon/n02086910_103.jpg")
# evaluate_model()