tf2.py

from __future__ import absolute_import, division, print_function

import os

import matplotlib.pylab as plt
import numpy as np

import tensorflow as tf
import tensorflow_hub as hub

print("Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print("Hub version: ", hub.__version__)
print("GPU is", "available" if tf.test.is_gpu_available() else "NOT AVAILABLE")

module_selection = ("inception_v3", 299, 2048) #@param ["(\"mobilenet_v2\", 224, 1280)", "(\"inception_v3\", 299, 2048)"] {type:"raw", allow-input: true}
handle_base, pixels, FV_SIZE = module_selection
MODULE_HANDLE ="https://tfhub.dev/google/tf2-preview/{}/feature_vector/2".format(handle_base)
IMAGE_SIZE = (pixels, pixels)
print("Using {} with input size {} and output dimension {}".format(MODULE_HANDLE, IMAGE_SIZE, FV_SIZE))

BATCH_SIZE = 32 #@param {type:"integer"}

data_dir='/Users/sheldon/pythonProjects/hub/examples/image_retraining/flower_photos'
datagen_kwargs = dict(rescale=1./255, validation_split=.20)
valid_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
    **datagen_kwargs)
valid_generator = valid_datagen.flow_from_directory(
    data_dir, subset="validation", shuffle=False,
    target_size=IMAGE_SIZE, batch_size=BATCH_SIZE)

do_data_augmentation = False #@param {type:"boolean"}
if do_data_augmentation:
  train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
      rotation_range=40,
      horizontal_flip=True,
      width_shift_range=0.2, height_shift_range=0.2,
      shear_range=0.2, zoom_range=0.2,
      **datagen_kwargs)
else:
  train_datagen = valid_datagen
train_generator = train_datagen.flow_from_directory(
    data_dir, subset="training", shuffle=True,
    target_size=IMAGE_SIZE, batch_size=BATCH_SIZE)

do_fine_tuning = False #@param {type:"boolean"}
print("Building model with", MODULE_HANDLE)
model = tf.keras.Sequential([
    hub.KerasLayer(MODULE_HANDLE, output_shape=[FV_SIZE],
                   trainable=do_fine_tuning),
    tf.keras.layers.Dropout(rate=0.2),
    tf.keras.layers.Dense(train_generator.num_classes, activation='softmax',
                          kernel_regularizer=tf.keras.regularizers.l2(0.0001))
])
model.build((None,)+IMAGE_SIZE+(3,))
model.summary()

model.compile(
  optimizer=tf.keras.optimizers.SGD(lr=0.005, momentum=0.9), 
  loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
  metrics=['accuracy'])

steps_per_epoch = train_generator.samples // train_generator.batch_size
validation_steps = valid_generator.samples // valid_generator.batch_size
hist = model.fit_generator(
    train_generator,
    epochs=5, steps_per_epoch=steps_per_epoch,
    validation_data=valid_generator,
    validation_steps=validation_steps).history

plt.figure()
plt.ylabel("Loss (training and validation)")
plt.xlabel("Training Steps")
plt.ylim([0,2])
plt.plot(hist["loss"])
plt.plot(hist["val_loss"])

plt.figure()
plt.ylabel("Accuracy (training and validation)")
plt.xlabel("Training Steps")
plt.ylim([0,1])
plt.plot(hist["accuracy"])
plt.plot(hist["val_accuracy"])