Upload 787antitheft_195.py

787antitheft_195.py

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+# -*- coding: utf-8 -*-
+"""787antitheft.195
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1RuQfAM5faBjQTkTWdhahfka6eF7S0MGu
+"""
+
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+
+mnist = tf.keras.datasets.mnist
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+x_train = tf.keras.utils.normalize(x_train, axis=1)
+x_test = tf.keras.utils.normalize(x_test, axis=1)
+
+model = tf.keras.models.Sequential()
+model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
+model.add(tf.keras.layers.Dense(128, activation='relu'))
+model.add(tf.keras.layers.Dense(128, activation='relu'))
+model.add(tf.keras.layers.Dense(10, activation='softmax'))
+
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+model.fit(x_train, y_train, epochs=3)
+
+model.save('handwritten.model')
+
+model = tf.keras.models.load_model('handwritten.model')
+
+loss, accimaguracy = model.evaluate(x_test, y_test)
+
+image_number = 1
+while os.path.isfile(f"digits/digit{image_number}.png"):
+  try:
+    img = cv2.imread(f"digit/digits{image_number}.png")[:,:,0]
+    img = np.invert(np.array([img]))
+    prediction = model.predict(img)
+    print(f"This digit is probably a {np.argmax(prediction)}")
+    plt.imshow(img[0], cmap=plt.cm.binary)
+    plt.show()
+  except:
+    print("Error!")
+  finally:
+    image_number += 1
+