example of a deep learning algorithm in Python - Convolutional Neural Network (CNN)

Author: zahariev-webbersof

convolutional_neural_network_CNN_algorithm_exampe.py

@@ -0,0 +1,53 @@
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten
+
+# Initializing the CNN
+classifier = Sequential()
+
+# Step 1 - Convolution
+classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
+
+# Step 2 - Pooling
+classifier.add(MaxPooling2D(pool_size = (2, 2)))
+
+# Adding a second convolutional layer
+classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
+classifier.add(MaxPooling2D(pool_size = (2, 2)))
+
+# Step 3 - Flattening
+classifier.add(Flatten())
+
+# Step 4 - Full connection
+classifier.add(Dense(units = 128, activation = 'relu'))
+classifier.add(Dense(units = 1, activation = 'sigmoid'))
+
+# Compiling the CNN
+classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
+
+# Fitting the CNN to the images
+
+from keras.preprocessing.image import ImageDataGenerator
+
+train_datagen = ImageDataGenerator(rescale = 1./255,
+                                   shear_range = 0.2,
+                                   zoom_range = 0.2,
+                                   horizontal_flip = True)
+
+test_datagen = ImageDataGenerator(rescale = 1./255)
+
+training_set = train_datagen.flow_from_directory('dataset/training_set',
+                                                 target_size = (64, 64),
+                                                 batch_size = 32,
+                                                 class_mode = 'binary')
+
+test_set = test_datagen.flow_from_directory('dataset/test_set',
+                                            target_size = (64, 64),
+                                            batch_size = 32,
+                                            class_mode = 'binary')
+
+classifier.fit_generator(training_set,
+                         steps_per_epoch = 8000,
+                         epochs = 25,
+                         validation_data = test_set,
+                         validation_steps = 2000)