What is CNN?
A CNN is trained on classified images and makes predictions based on them.
After going through a CNN consisting of Convolution, Pooling, Flattening, and Full Connection steps, image above is changed to:
Example
Code
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import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
# image preprocessing
train_datagen = ImageDataGenerator (
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
training_set= train_datagen.flow_from_directory(
'dataset/training_set',
target_size=(64, 64),
batch_size=32,
class_mode='binary'
)
test_datagen = ImageDataGenerator(rescale=1./255)
test_set = test_datagen.flow_from_directory(
'dataset/test_set',
target_size=(64, 64),
batch_size=32,
class_mode='binary'
)
# Build a model
cnn = tf.keras.models.Sequential()
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[64,64,3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(tf.keras.layers.Flatten())
cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))
cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))
cnn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
test_image = tf.keras.utils.load_img(path='dataset/single_prediction/cat_or_dog_1.jpg', target_size=(64,64))
test_image = tf.keras.utils.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
result = cnn.predict(test_image)
if result[0][0] == 1:
prediction='dog'
else:
prediction='cat'
print(prediction)
Result
1
dog