Convolutional Layer (Conv2D)
- 지역적인 특징 추출
- technique to isolate features in images
www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D
tf.keras.layers.Conv2D | TensorFlow Core v2.4.0
2D convolution layer (e.g. spatial convolution over images).
www.tensorflow.org
Pooling Layer (MaxPool2D)
- preserve that were highlighted by the convolution
- downsize images
- reduce information in an image while maintaining features
www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D
tf.keras.layers.MaxPool2D | TensorFlow Core v2.4.0
Max pooling operation for 2D spatial data.
www.tensorflow.org
Code
import tensorflow as tf
# 1. data
mnist = tf.keras.datasets.fashion_mnist
(train_X, train_y), (test_X, test_y) = mnist.load_data()
train_X = train_X.reshape(60000, 28, 28, 1)
test_X = test_X.reshape(10000, 28, 28, 1)
train_X = train_X / 255.0
test_X = test_X / 255.0
# 2. model
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(64, (3, 3), activation='relu', input_shae=(28, 28, 1)),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
# 3. compile
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['acc'])
# 4. training
model.fit(train_X, train_y, epochs=5)
# 5. evaluate
loss = model.evaluate(test_X, test_y)
print(loss)
convolutional layer 통과한 뒤 width, height 계산
- new_width = (width+2*p-f)/s+1
- new_height = (height+2*p-f)/s+1
- * p = padding
- * f = filter
- * s = stride
convolution 관련 개념 강의
www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF
Convolutional Neural Networks (Course 4 of the Deep Learning Specialization)
www.youtube.com
* reference