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"""787antitheft.195 |
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Automatically generated by Colab. |
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Original file is located at |
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https://colab.research.google.com/drive/1RuQfAM5faBjQTkTWdhahfka6eF7S0MGu |
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""" |
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import os |
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import cv2 |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import tensorflow as tf |
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mnist = tf.keras.datasets.mnist |
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(x_train, y_train), (x_test, y_test) = mnist.load_data() |
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x_train = tf.keras.utils.normalize(x_train, axis=1) |
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x_test = tf.keras.utils.normalize(x_test, axis=1) |
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model = tf.keras.models.Sequential() |
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model.add(tf.keras.layers.Flatten(input_shape=(28,28))) |
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model.add(tf.keras.layers.Dense(128, activation='relu')) |
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model.add(tf.keras.layers.Dense(128, activation='relu')) |
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model.add(tf.keras.layers.Dense(10, activation='softmax')) |
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) |
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model.fit(x_train, y_train, epochs=3) |
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model.save('handwritten.model') |
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model = tf.keras.models.load_model('handwritten.model') |
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loss, accimaguracy = model.evaluate(x_test, y_test) |
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image_number = 1 |
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while os.path.isfile(f"digits/digit{image_number}.png"): |
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try: |
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img = cv2.imread(f"digit/digits{image_number}.png")[:,:,0] |
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img = np.invert(np.array([img])) |
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prediction = model.predict(img) |
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print(f"This digit is probably a {np.argmax(prediction)}") |
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plt.imshow(img[0], cmap=plt.cm.binary) |
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plt.show() |
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except: |
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print("Error!") |
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finally: |
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image_number += 1 |
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