Smiril
31 lines
971 B
Python
31 lines
971 B
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: m1_test.py
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# Created Date: Saturday July 9th 2022
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# Author: Smiril
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# Email: sonar@gmx.com
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#############################################################
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import tensorflow as tf
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tf.__version__
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tf.config.list_physical_devices()
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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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model = tf.keras.models.Sequential([
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tf.keras.layers.Flatten(input_shape=(28, 28)),
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dropout(0.2),
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tf.keras.layers.Dense(10),
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tf.keras.layers.Dropout(0.2),
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tf.keras.layers.Dense(2,activation=tf.nn.softmax)
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])
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loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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model.compile(optimizer='adam',
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loss=loss_fn,
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metrics=['accuracy'])
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model.fit(x_train, y_train, epochs=10)
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model.build()
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model.summary()
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