Update Train_code_mobilenet

Train_code_mobilenet

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+import tensorflow as tf
+from tensorflow.keras.applications import MobileNet
+from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
+from tensorflow.keras.models import Model
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Load the MobileNet base model
+base_model = MobileNet(weights='imagenet', include_top=False)
+
+# Add custom classification layers
+x = base_model.output
+x = GlobalAveragePooling2D()(x)
+x = Dense(1024, activation='relu')(x)
+num_classes=2
+predictions = Dense(num_classes, activation='softmax')(x)
+
+model = Model(inputs=base_model.input, outputs=predictions)
+
+# Compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Data augmentation and preprocessing
+
+
+train_datagen = ImageDataGenerator(
+    preprocessing_function=tf.keras.applications.mobilenet.preprocess_input,
+    rotation_range=20,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    horizontal_flip=True
+)
+batch_size=16
+train_generator = train_datagen.flow_from_directory(
+    '/content/tire-dataset/train_data',
+    target_size=(224, 224),
+    batch_size=batch_size,
+    class_mode='categorical'
+)
+
+test_datagen = ImageDataGenerator(
+    preprocessing_function=tf.keras.applications.mobilenet.preprocess_input,
+    rotation_range=20,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    horizontal_flip=True
+)
+batch_size=16
+
+
+# Train the model
+num_epochs=1
+model.fit(train_generator, epochs=num_epochs)
+
+# Evaluate the model on the test set
+test_generator = test_datagen.flow_from_directory(
+    '/content/tire-dataset/test_data',
+    target_size=(224, 224),
+    batch_size=batch_size,
+    class_mode='categorical'
+)
+
+accuracy = model.evaluate(test_generator)
+print('Test accuracy:', accuracy)
+
+
+
+from tensorflow import keras
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.mobilenet import preprocess_input, decode_predictions
+import numpy as np
+
+# Load the model
+#model = keras.models.load_model('path_to_your_model.h5')
+
+# Load and preprocess an image for inference
+img_path = '/content/tire-dataset/test_data/Tire/00000.jpg'
+img = image.load_img(img_path, target_size=(224, 224))
+x = image.img_to_array(img)
+x = np.expand_dims(x, axis=0)
+x = preprocess_input(x)
+
+# Make a prediction
+predictions = model.predict(x)
+
+# Decode and display the prediction
+# decoded_predictions = decode_predictions(predictions, top=3)[0]
+# for label, description, score in decoded_predictions:
+#     print(f'{label}: {description} ({score:.2f})')
+
+
+model.save('/content/model_keras/keras_model.h5')
+
+
+!tensorflowjs_converter --input_format=keras --output_format=tfjs_graph_model --split_weights_by_layer --weight_shard_size_bytes=99999999 --quantize_float16=* /content/model_keras/keras_model.h5 ./model_tfjs
+