ResNet50_train.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


from tensorflow.keras.layers import Conv2D, Flatten, Dense, MaxPool2D, BatchNormalization, GlobalAveragePooling2D
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
from tensorflow.keras.models import Sequential, Model

import matplotlib.pyplot as plt
import numpy as np

import splitfolders


# In[2]:


SEED = 42

TRAIN_R = 0.6  # Train ratio
VAL_R = 0.2
TEST_R = 0.2

IMG_HEIGHT, IMG_WIDTH = (224, 224)
BATCH_SIZE = 32


OUTPUT_DIR = "processed_data"


train_data_dir = f"{OUTPUT_DIR}/train"
valid_data_dir = f"{OUTPUT_DIR}/val"
test_data_dir = f"{OUTPUT_DIR}/test"


# In[3]:


train_datagen = ImageDataGenerator(
    preprocessing_function=preprocess_input,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(
    preprocessing_function=preprocess_input)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(IMG_HEIGHT, IMG_WIDTH),
    batch_size=BATCH_SIZE,
    class_mode="categorical")

valid_generator = train_datagen.flow_from_directory(
    valid_data_dir,
    target_size=(IMG_HEIGHT, IMG_WIDTH),
    batch_size=BATCH_SIZE,
    class_mode="categorical")


test_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(IMG_HEIGHT, IMG_WIDTH),
    batch_size=1,
    class_mode="categorical")


# In[4]:


EPOCHS = 8

base_model = ResNet50(include_top=False, weights="imagenet")

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation="relu")(x)

predictions = Dense(train_generator.num_classes, activation="softmax")(x)
model = Model(inputs=base_model.input, outputs=predictions)

for layer in base_model.layers:
    layer.trainable = False
    
model.compile(
    optimizer="adam",
    loss="categorical_crossentropy",
    metrics=["acc"])




history = model.fit(train_generator,
                    validation_data=valid_generator,
                    epochs=EPOCHS)


# In[5]:


model.save('Saved_Model\ResNet50_ton.h5')


# In[6]:


test_loss, test_acc = model.evaluate(test_generator,verbose=2)
print('\nTest accuracy:', test_acc)