Olexiy troschij

tasks/OlexiyTr_code/task_3/transfer_learning.py

@@ -0,0 +1,141 @@
+# -*- coding: utf-8 -*-
+"""Transfer Learning
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/11W4P9OMoLafNSM5TWLVsR095Bgxe7t2e
+"""
+
+from google.colab import drive
+drive.mount('/content/gdrive')
+
+PATH_TO_DATA = "/content/gdrive/MyDrive/data" 
+!ls {PATH_TO_DATA}
+
+import numpy as np 
+import pandas as pd 
+import os
+import cv2
+
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from tensorflow.keras import layers
+from tensorflow.keras.preprocessing import image_dataset_from_directory
+
+BATCH_SIZE = 32
+IMG_SIZE = (160,160)
+
+train_dataset = image_dataset_from_directory(PATH_TO_DATA,
+                                              validation_split=0.3,
+                                              subset= "training",
+                                              seed=42,
+                                              image_size= IMG_SIZE,
+                                              batch_size=BATCH_SIZE)
+
+validation_dataset = image_dataset_from_directory(PATH_TO_DATA,
+                                                  validation_split = 0.3,
+                                                  shuffle=True,
+                                                  subset= "validation",
+                                                  seed=42,
+                                                  batch_size=BATCH_SIZE,
+                                                  image_size=IMG_SIZE
+                                                  )
+
+val_batches = tf.data.experimental.cardinality(validation_dataset)
+test_dataset = validation_dataset.take(val_batches // 5)
+validation_dataset = validation_dataset.skip(val_batches // 5)
+
+print('Number of validation batches: %d' % tf.data.experimental.cardinality(validation_dataset))
+print('Number of test batches: %d' % tf.data.experimental.cardinality(test_dataset))
+
+AUTOTUNE = tf.data.AUTOTUNE
+
+train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)
+validation_dataset = validation_dataset.prefetch(buffer_size=AUTOTUNE)
+test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)
+
+data_augmentation = tf.keras.Sequential([
+  tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),
+  tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
+])
+
+preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input
+
+rescale = tf.keras.layers.experimental.preprocessing.Rescaling(1./127.5, offset= -1)
+
+IMG_SHAPE = IMG_SIZE + (3,)
+base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
+                                               include_top=False,
+                                               weights='imagenet')
+
+image_batch, label_batch = next(iter(train_dataset))
+feature_batch = base_model(image_batch)
+print(feature_batch.shape)
+
+base_model.trainable = False
+
+base_model.summary()
+
+global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
+feature_batch_average = global_average_layer(feature_batch)
+print(feature_batch_average.shape)
+
+prediction_layer = tf.keras.layers.Dense(1)
+prediction_batch = prediction_layer(feature_batch_average)
+print(prediction_batch.shape)
+
+inputs = tf.keras.Input(shape=(160, 160, 3))
+x = data_augmentation(inputs)
+x = preprocess_input(x)
+x = base_model(x, training=False)
+x = global_average_layer(x)
+x = tf.keras.layers.Dropout(0.2)(x)
+outputs = prediction_layer(x)
+model = tf.keras.Model(inputs, outputs)
+
+base_learning_rate = 0.0001
+model.compile(optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate),
+              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+
+model.summary()
+
+len(model.trainable_variables)
+
+initial_epochs = 5
+
+loss0, accuracy0 = model.evaluate(validation_dataset)
+
+print("initial loss: {:.2f}".format(loss0))
+print("initial accuracy: {:.2f}".format(accuracy0))
+
+history = model.fit(train_dataset,
+                    epochs=initial_epochs,
+                    validation_data=validation_dataset)
+
+acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+
+plt.figure(figsize=(8, 8))
+plt.subplot(2, 1, 1)
+plt.plot(acc, label='Training Accuracy')
+plt.plot(val_acc, label='Validation Accuracy')
+plt.legend(loc='lower right')
+plt.ylabel('Accuracy')
+plt.ylim([min(plt.ylim()),1])
+plt.title('Training and Validation Accuracy')
+
+plt.subplot(2, 1, 2)
+plt.plot(loss, label='Training Loss')
+plt.plot(val_loss, label='Validation Loss')
+plt.legend(loc='upper right')
+plt.ylabel('Cross Entropy')
+plt.ylim([0,1.0])
+plt.title('Training and Validation Loss')
+plt.xlabel('epoch')
+plt.show()
+