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finger_recog.py
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finger_recog.py
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# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.models import model_from_json
# Initialising the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Convolution2D(32, 3, 3, input_shape = (128,128, 3), activation = 'relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second convolutional layer
#classifier.add(Convolution2D(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(output_dim = 256, activation = 'relu'))
classifier.add(Dense(output_dim = 6, activation = 'softmax'))
# Compiling the CNN adam
classifier.compile(optimizer = 'adam', loss = 'categorical_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('fin/train',
target_size = (128,128),
batch_size = 32,
class_mode = 'categorical')
test_set = test_datagen.flow_from_directory('fin/test',
target_size = (128,128),
batch_size = 32,
class_mode = 'categorical')
classifier.fit_generator(training_set,
steps_per_epoch = 9000,
nb_epoch = 25,
validation_data = test_set,
nb_val_samples = 1800)
# serialize model to JSON
model_json = classifier.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
classifier.save_weights("model.h5")
print("Saved model to disk")