Smiril
41 lines
1.5 KiB
Python
41 lines
1.5 KiB
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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logits = [[4.0, 2.0, 1.0], [0.0, 5.0, 1.0]]
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inputs = tf.keras.Input(shape=(784,), name="digits")
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model = tf.keras.models.load_model('model')
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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='selu',name='layer1'),
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tf.keras.layers.Dropout(0.2),
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tf.keras.layers.Dense(64,activation='relu',name='layer2'),
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tf.keras.layers.Dropout(0.2),
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tf.keras.layers.Dense(32,activation='elu',name='layer3'),
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tf.keras.layers.Dropout(0.2),
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tf.keras.layers.Dense(16,activation='tanh',name='layer4'),
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tf.keras.layers.Dropout(0.2),
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tf.keras.layers.Dense(8,activation='sigmoid',name='layer5'),
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tf.keras.layers.Dropout(0.2)
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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_test, y_test, epochs=10)
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outputs = tf.keras.layers.Dense(4, activation="softmax", name="predictions")(x_test)
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model = tf.keras.Model(inputs=inputs, outputs=outputs)
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model.build()
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model.save('model')
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model.summary()
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