intel_image_classification/using_transfer_learning.py at main · saatweek/intel_image_classification · GitHub

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import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import plotly.graph_objects as go
import tensorflow_hub as hub
import kagglehub
import os

file_path = kagglehub.dataset_download("puneet6060/intel-image-classification")
# model_path = kagglehub.model_download("tensorflow/inception/tfLite/v4")

train_dir = os.path.join(file_path, os.path.join("seg_train", "seg_train"))
validation_dir = os.path.join(file_path, os.path.join("seg_test", "seg_test"))

train_datagen = ImageDataGenerator(rescale = 1./255, rotation_range=0.3, width_shift_range=0.3,
    height_shift_range=0.3,shear_range=0.3, zoom_range=0.3, horizontal_flip=True)

validation_datagen = ImageDataGenerator(rescale = 1./255, rotation_range=0.3, width_shift_range=0.3,
    height_shift_range=0.3,shear_range=0.3, zoom_range=0.3, horizontal_flip=True)

train_generator = train_datagen.flow_from_directory(train_dir, target_size = (150, 150), batch_size = 128, class_mode='sparse')

validation_generator = validation_datagen.flow_from_directory(validation_dir, target_size = (150, 150), batch_size = 128, class_mode='sparse')

url = 'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/4'

base_model = hub.KerasLayer(url, input_shape = (150, 150, 3))

model = tf.keras.Sequential([base_model,
                             tf.keras.layers.Dense(128, activation='relu'),
                             tf.keras.layers.Dense(64, activation='relu'),
                             tf.keras.layers.Dense(6, activation='softmax')])
model.summary()
model.compile(loss = 'sparse_categorical_crossentropy', optimizer='adam', metrics = 'acc')

history = model.fit(train_generator, steps_per_epoch=20, epochs=10, validation_data = validation_generator)

fig = go.Figure()
x = np.linspace(1,10, 10)
y1 = history.history['acc']
y2 = history.history['val_acc']
fig.add_trace(go.Scatter(x = x, y = y1, name = 'training accuracy')),
fig.add_trace(go.Scatter(x = x, y = y2, name = 'validation accuracy')),
fig.update_layout(xaxis_title = 'Epochs', yaxis_title = 'accuracy', title = 'Accuracy of Model')
fig.show()