Create 13_TransferLearning.py

pages/13_TransferLearning.py

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+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras import layers, models, applications
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Set dataset paths
+train_dir = 'data/train'
+validation_dir = 'data/validation'
+
+# Streamlit app
+st.title("Transfer Learning with VGG16 for Image Classification")
+
+# Input parameters
+batch_size = st.slider("Batch Size", 16, 128, 32, 16)
+epochs = st.slider("Epochs", 5, 50, 10, 5)
+
+# Data augmentation and preprocessing
+train_datagen = ImageDataGenerator(
+    rescale=1./255,
+    rotation_range=40,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True,
+    fill_mode='nearest'
+)
+
+validation_datagen = ImageDataGenerator(rescale=1./255)
+
+train_generator = train_datagen.flow_from_directory(
+    train_dir,
+    target_size=(150, 150),
+    batch_size=batch_size,
+    class_mode='binary'
+)
+
+validation_generator = validation_datagen.flow_from_directory(
+    validation_dir,
+    target_size=(150, 150),
+    batch_size=batch_size,
+    class_mode='binary'
+)
+
+# Load the pre-trained VGG16 model
+base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
+
+# Freeze the convolutional base
+base_model.trainable = False
+
+# Add custom layers on top
+model = models.Sequential([
+    base_model,
+    layers.Flatten(),
+    layers.Dense(256, activation='relu'),
+    layers.Dropout(0.5),
+    layers.Dense(1, activation='sigmoid')  # Change the output layer based on the number of classes
+])
+
+model.summary()
+
+# Compile the model
+model.compile(optimizer='adam',
+              loss='binary_crossentropy',  # Change loss function based on the number of classes
+              metrics=['accuracy'])
+
+# Train the model
+if st.button("Train Model"):
+    with st.spinner("Training the model..."):
+        history = model.fit(
+            train_generator,
+            steps_per_epoch=train_generator.samples // train_generator.batch_size,
+            epochs=epochs,
+            validation_data=validation_generator,
+            validation_steps=validation_generator.samples // validation_generator.batch_size
+        )
+        
+        st.success("Model training completed!")
+
+    # Display training curves
+    st.subheader("Training and Validation Accuracy")
+    fig, ax = plt.subplots()
+    ax.plot(history.history['accuracy'], label='Training Accuracy')
+    ax.plot(history.history['val_accuracy'], label='Validation Accuracy')
+    ax.set_xlabel('Epoch')
+    ax.set_ylabel('Accuracy')
+    ax.legend()
+    st.pyplot(fig)
+
+    st.subheader("Training and Validation Loss")
+    fig, ax = plt.subplots()
+    ax.plot(history.history['loss'], label='Training Loss')
+    ax.plot(history.history['val_loss'], label='Validation Loss')
+    ax.set_xlabel('Epoch')
+    ax.set_ylabel('Loss')
+    ax.legend()
+    st.pyplot(fig)
+
+# Evaluate the model
+if st.button("Evaluate Model"):
+    test_loss, test_acc = model.evaluate(validation_generator, verbose=2)
+    st.write(f"Validation accuracy: {test_acc}")