47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
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%matplotlib inline
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from tensorflow.examples.tutorials.mnist import input_data
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import numpy as np
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import matplotlib.pyplot as plt
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mnist = input_data.read_data_sets('./SecondSunday/mnist_data', one_hot=True)
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label_number = mnist.train.labels.argmax(axis=1)
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number_imgs = {str(i):mnist.train.images[np.argwhere(label_number == i).squeeze()] for i in range(10)}
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DATA_COUNT = 100
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phone_number_digits = np.random.randint(10**9,10**10,size=(DATA_COUNT,10))
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phone_number_digits = np.random.randint(10,size=(DATA_COUNT,10))
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phone_number_digits.astype(str)
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phone_number_strings = phone_number_digits.astype(str)
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def pick_img(num):
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rand_idx = np.random.randint(number_imgs[num].shape[0])
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img = number_imgs[num][rand_idx].reshape(28,28)
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return img
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def create_phone_img(phon_no):
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return np.hstack(tuple([pick_img(d) for d in phon_no]))
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def create_phone_images(phone_array):
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phone_number_images = []
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for phon_no in phone_array:
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phone_number_images.append(create_phone_img(phon_no))
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return np.array(phone_number_images).reshape(-1,28*280)
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phone_number_imgs = create_phone_images(phone_number_strings)
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from keras.models import Sequential
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from keras.layers import Dense, Activation
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model = Sequential([
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Dense(32, input_shape=(7840,)),
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Activation('relu'),
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Dense(10),
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Activation('linear'),
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])
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model.compile(optimizer='rmsprop',
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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model.fit()
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# plt.imshow(phone_number_imgs[np.random.randint(phone_number_imgs.shape[0])])
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