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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.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10, validation_split=0.1)
# 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/imgclassifiertrainingsample",
commit_message="Optional commit message",
tags=["image-classifier", "some_other_tag"],
include_optimizer=True,token=sac
)
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