sprite_model.py

# -*- coding: utf-8 -*-
"""sprite_preprocessing_protomodel.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1mOBKUwc1RGFROZN4DNyUdWqCBnPfSzS9
"""

import PIL
import tensorflow as tf
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import pathlib

print(tf.__version__)

# image file path saved, quantity of images stored and printed
data_dir = pathlib.Path('images')
image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)

#sprite file path saved, sprite image loaded
sprite = list(data_dir.glob('sprite/*'))
PIL.Image.open(str(sprite[1]))

#no-event file path saved, no event image loaded
noevent = list(data_dir.glob('noevent/*'))
PIL.Image.open(str(noevent[50]))

#creates a "training" tf.data.Dataset
#validation_split is an optional float between 0-1 reserved for validation
#seed is optional random seed for shuffling/transformations
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123)

#creates a "training" tf.data.Dataset
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123)

class_names = train_ds.class_names
print(class_names)
num_classes = len(class_names)

# plt.figure(figsize=(10, 10))
# for images, labels in train_ds.take(1):
#   for i in range(9):
#     ax = plt.subplot(3, 3, i + 1)
#     plt.imshow(images[i].numpy().astype("uint8"))
#     plt.title(class_names[labels[i]])
#     plt.axis("off")
#     plt.show()

# for image_batch, labels_batch in train_ds:
#   print(image_batch.shape)
#   print(labels_batch.shape)
#   break

AUTOTUNE = tf.data.experimental.AUTOTUNE

train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
#Preformance increase https://www.tensorflow.org/tutorials/load_data/images

model = tf.keras.Sequential([
  layers.experimental.preprocessing.Rescaling(1./255),
  layers.Conv2D(32, 3, activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(32, 3, activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(32, 3, activation='relu'),
  layers.MaxPooling2D(),
  layers.Flatten(),
  layers.Dense(128, activation='relu'),
  layers.Dense(num_classes)
])

model.compile(
  optimizer='adam',
  loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
  metrics=['accuracy'])

model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=4
)

prediction = model.predict(val_ds)