cnns_object_binary_classifications.py

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.callbacks import EarlyStopping
import pathlib
import matplotlib.pyplot as plt

data_dir = './images'
data_dir = pathlib.Path(data_dir)

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(256, 256),
    batch_size=64
)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(256,256),
    batch_size=64
)

class_names = train_ds.class_names
print(class_names)

AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

model = tf.keras.Sequential([
    layers.experimental.preprocessing.Rescaling(1./255),
    layers.Conv2D(64, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Dropout(0.2),
    layers.Conv2D(64, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Dropout(0.2),
    layers.Conv2D(64, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Dropout(0.2),
    layers.Conv2D(32, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Dropout(0.2),
    layers.Conv2D(32, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Dropout(0.2),
    layers.Conv2D(32, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Dropout(0.2),
    layers.Conv2D(32, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Dropout(0.2),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(16, activation='relu'),
    layers.Dense(2) #numer of classes
])

model.compile(optimizer='adam', loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
history = model.fit(train_ds, validation_data=val_ds, epochs=100)
y_vloss = history.history['val_loss']
y_loss = history.history['loss']
y_acc = history.history['accuracy']
y_vacc = history.history['val_accuracy']

fig, (ax1, ax2) = plt.subplots(1,2)
ax1.plot(np.arange(len(y_vloss)), y_vloss, marker='.', c='red')
ax1.plot(np.arange(len(y_loss)), y_loss, marker='.', c='blue')
ax1.grid()
plt.setp(ax1, xlabel='epoch', ylabel='loss')

ax2.plot(np.arange(len(y_vacc)), y_vacc, marker='.', c='red')
ax2.plot(np.arange(len(y_acc)), y_acc, marker='.', c='blue')
ax2.grid()
plt.setp(ax2, xlabel='epoch', ylabel='loss')

plt.show()