trainer.py

from huggingface_hub import push_to_hub_keras
import numpy as np
import pandas as pd
import os
from sklearn.metrics import classification_report
import seaborn as sn
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import cv2
import tensorflow as tf
from tqdm import tqdm

sac = os.getenv('accesstoken')
sn.set(font_scale=1.4)

class_names = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']
class_names_label = {class_name: i for i, class_name in enumerate(class_names)}
nb_classes = len(class_names)
print(class_names_label)
IMAGE_SIZE = (150, 150)


def load_data():
    DIRECTORY = "imgdataset"
    CATEGORY = ["seg_train", "seg_test"]
    output = []
    for category in CATEGORY:
        path = os.path.join(DIRECTORY, category)
        images = []
        labels = []
        print("Loading {}".format(category))
        for folder in os.listdir(path):
            label = class_names_label[folder]
            # Iterate through each image in our folder
            for file in os.listdir(os.path.join(path, folder)):
                # Get the path name of the image
                img_path = os.path.join(os.path.join(path, folder), file)
                # Open and resize the ing
                image = cv2.imread(img_path)
                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
                image = cv2.resize(image, IMAGE_SIZE)
                # Append the image and its corresponding Label to the output
                images.append(image)
                labels.append(label)
        # Convert both the images and labels to a numpy array
        images = np.array(images, dtype='float32')
        labels = np.array(labels, dtype='int32')
        output.append((images, labels))
    return output


(train_images, train_labels), (test_images, test_labels) = load_data()
train_images, train_labels = shuffle(train_images, train_labels, random_state=25)
print("Train: ", train_images.shape, train_labels.shape)
print("Test: ", test_images.shape, test_labels.shape)
#
# model = tf.keras.models.Sequential([
#     tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
#     tf.keras.layers.MaxPooling2D(2, 2),
#     tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
#     tf.keras.layers.MaxPooling2D(2, 2),
#     tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
#     tf.keras.layers.MaxPooling2D(2, 2),
#     tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
#     tf.keras.layers.MaxPooling2D(2, 2),
#     tf.keras.layers.Flatten(),
#     tf.keras.layers.Dense(512, activation='relu'),
#     tf.keras.layers.Dense(6, activation='softmax')
# ])
model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 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.Flatten(),
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(6, activation=tf.nn.softmax)
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=6, validation_split=0.2)

# Evaluate the model
model.evaluate(test_images, test_labels)

# save the model
model.save("model.keras")
# from transformers import push_to_hub_keras

# Save the model
# model.save("model.keras")

# Upload the model to your Hugging Face space repository
push_to_hub_keras(
    model,
    repo_id="okeowo1014/imgclassifiertraining",
    commit_message="Optional commit message",
    tags=["image-classifier", "some_other_tag"],
    include_optimizer=True, token=sac
)