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import tensorflow as tf import numpy as np import matplotlib.pyplot as plt (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data() train_images=train_images/255 test_images=test_images/255 for i in range(0, len(train_images)): for j in range(0,28): for k in range(0,28): if train_images[i][j][k] > 0.6: train_images[i][j][k] = 1 else: train_images[i][j][k] = 0 train_labels = tf.keras.utils.to_categorical(train_labels, 10) model = tf.keras.Sequential([ tf.keras.Input(shape=(28, 28, 1)), tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation="relu"), tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"), tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), tf.keras.layers.Flatten(), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(10, activation="softmax"), ]) model.compile(optimizer="adam",loss="categorical_crossentropy", metrics=["accuracy"]) model.fit(train_images, train_labels, epochs=10)
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