<jupyter_start><jupyter_text><jupyter_code>from google.colab import drive
drive.mount('/content/drive', force_remount=True)

gdrive_home_dir = '/content/drive/My Drive/'
!ls '/content/drive/My Drive/dataset_fp/labelfinger.csv'
!ls '/content/drive/My Drive/dataset_fp/data/'

from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility
import pandas as pd
from PIL import Image
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
batch_size = 10
nb_classes = 10
nb_epoch = 2
# To make the data ready for CNN, pictures are named with indexes, like '1.jpg', '2.jpg', etc..
def dir_to_dataset(mypath, loc_train_labels=""):
    dataset = []
    
    gbr = pd.read_csv(loc_train_labels, sep="\t")

    #for file_count, file_name in enumerate( sorted(glob(glob_files),key=len) ):
    for i in range(1,81):
        image = Image.open(mypath + str(i)+'.jpg')
        img = Image.open(mypath + str(i)+'.jpg').convert('LA') #tograyscale
        '''
        pixels_list = list(img.getdata())
        for pixel in pixels_list:
          p = pixel
          dataset.append(p)
        '''
        #pixels_list = list(img.getdata())
        #width, height = img.size
        #pixels = [pixels[i * width:(i + 1) * width] for i in range(height)]
        
        pixels = [f[0] for f in list(img.getdata())] #<- original
        dataset.append(pixels)
    # outfile = glob_files+"out"
    # np.save(outfile, dataset)
    if len(loc_train_labels) > 0:
        df = pd.read_csv(loc_train_labels)
        return np.array(dataset), gbr["Class"].values
    else:
        return np.array(dataset)
if __name__ == '__main__':
    Data, y = dir_to_dataset("/content/drive/My Drive/dataset_fp/data/","/content/drive/My Drive/dataset_fp/labelfinger.csv")
    
    #Split the train set and validation set
    train_set_x = Data[:60]
    val_set_x = Data[10:]
    train_set_y = y[:60]
    val_set_y = y[10:]

    (X_train, y_train), (X_test, y_test) = (train_set_x,train_set_y),(val_set_x,val_set_y)

    # input image dimensions
    img_rows, img_cols = 640, 480
    # number of convolutional filters to use
    nb_filters = 32
    # size of pooling area for max pooling
    pool_size = (2, 2)
    # convolution kernel size
    kernel_size = (3, 3)
    
    

    # Checking if the backend is Theano or Tensorflow
    if K.image_dim_ordering() == 'th':
        X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
        X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
        input_shape = (1, img_rows, img_cols)
    else:
        X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
        X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
        input_shape = (img_rows, img_cols, 1) 

    X_train = X_train.astype('float32')
    X_test = X_test.astype('float32')
    X_train /= 255
    X_test /= 255
    print('X_train shape:', X_train.shape)
    print(X_train.shape[0], 'train samples')
    print(X_test.shape[0], 'test samples')

    # convert class vectors to binary class matrices
    Y_train = np_utils.to_categorical(y_train, nb_classes)
    Y_test = np_utils.to_categorical(y_test, nb_classes)
    
    model = Sequential()
    model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
                    border_mode='valid',
                    input_shape=input_shape))
    model.add(Activation('relu'))
    model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=pool_size))
    model.add(Dropout(0.25))

    model.add(Flatten())
    #model.add(Dense(128))
    model.add(Dense(32))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy',
            optimizer='sgd',
            metrics=['accuracy'])

    model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=(X_test, Y_test))
    score = model.evaluate(X_test, Y_test, verbose=0)
    
    print(model.summary())
    
    print('Test score:', score[0])
    print('Test accuracy:', score[1])<jupyter_output>X_train shape: (60, 640, 480, 1)
60 train samples
70 test samples
{'fraction_non_alphanumeric': 0.12846517917511832, 'fraction_numerical': 0.021410863195853055, 'mean_word_length': 3.350980392156863, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '379471', 'license': 'MIT', 'n_tokens_mistral': 1649, 'n_tokens_neox': 1563, 'n_words': 334, 'path': 'cnn_fingerprint.ipynb', 'repo_name': 'connorj4/serial-multibiometric-face-fingerprint', 'size': '4437'}