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@@ -1,9 +1,17 @@
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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, x_test = x_train / 255.0, x_test / 255.0
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+(images_train, labels_train), (images_test, labels_test) = mnist.load_data()
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+
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+plt.figure()
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+plt.imshow(images_train[0])
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+plt.colorbar()
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+plt.grid(False)
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+plt.show()
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+
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+images_train, images_test = images_train / 255.0, images_test / 255.0
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model = tf.keras.models.Sequential([
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tf.keras.layers.Flatten(input_shape=(28, 28)),
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@@ -12,21 +20,15 @@ model = tf.keras.models.Sequential([
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tf.keras.layers.Dense(10)
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])
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-predictions = model(x_train[:1]).numpy()
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-print(predictions)
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-
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-predictions_softmax = tf.nn.softmax(predictions).numpy()
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-print(predictions_softmax)
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-
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-loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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-loss = loss_fn(y_train[:1], predictions).numpy()
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-print(loss)
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+model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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+ metrics=['accuracy'])
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+model.fit(images_train, labels_train, epochs=5, verbose=0, use_multiprocessing=True)
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+model.evaluate(images_test, labels_test, verbose=1)
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-model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy'])
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-model.fit(x_train, y_train, epochs=25)
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-model.evaluate(x_test, y_test, verbose=2)
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probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
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-test0 = probability_model(x_test[:5])
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-print(test0)
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+for i, n in enumerate(labels_test[0:10]):
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+ p: tf.Tensor = probability_model(images_test[i: i + 1])
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+ pv = p.numpy().flatten().tolist()
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+ print(pv.index(max(pv)), n)
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print("doen")
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