43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
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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import random
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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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def recognition():
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image_number = random.randint(1, 9)
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try:
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img = cv2.imread(f"digits/digit{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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loss, accuracy = model.evaluate(x_test, y_test)
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print(loss)
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print(accuracy)
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recognition() |