In [1]:
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import concatenate
In [14]:
import pathlib
data_dir = pathlib.Path("images")
In [16]:
image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)
warhol = list(data_dir.glob('Andy_Warhol/*'))
PIL.Image.open(str(warhol[0]))
Out[16]:
In [17]:
batch_size = 4
img_height = 180
img_width = 180
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split = 0.2,
subset = "training",
seed = 123,
image_size = (img_height, img_width),
batch_size = batch_size
)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split = 0.2,
subset = "validation",
seed = 123,
image_size = (img_height, img_width),
batch_size = batch_size
)
class_names = train_ds.class_names
print(class_names)
In [42]:
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
In [43]:
inputShape = (180, 180, 3)
num_classes = 51
model = Sequential([
layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Dropout(0.2),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(num_classes)
])
In [44]:
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.summary()
In [45]:
#this step may take a while
epochs = 10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)