4B_cnntest.py

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

# Initialising 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'])


# Part 2 - 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(training_set)
print(classifier.summary())

# Part 3 - Making new predictions
import numpy as np
from keras.preprocessing import image

for i in range(1, 12):
    test_image = image.load_img('dataset/single_prediction/sp' + str(i) + '.jpg', target_size=(64, 64))
    test_image = image.img_to_array(test_image)
    test_image = np.expand_dims(test_image, axis=0)
    result = classifier.predict(test_image)
    print(result[0][0])
    if result[0][0] == 1:
        prediction = 'washing'
    else:
        prediction = 'steady'
    print(prediction)