Smiril
74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: m1_tf_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 sys
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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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from random import randrange
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def xatoi(Str):
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sign, base, i = 1, 0, 0
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# If whitespaces then ignore.
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while (Str[i] == ' '):
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i += 1
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# Sign of number
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if (Str[i] == '-' or Str[i] == '+'):
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sign = 1 - 2 * (Str[i] == '-')
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i += 1
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# Checking for valid input
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while (i < len(Str) and
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Str[i] >= '0' and Str[i] <= '9'):
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# Handling overflow test case
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if (base > (sys.maxsize // 10) or
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(base == (sys.maxsize // 10) and
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(Str[i] - '0') > 7)):
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if (sign == 1):
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return sys.maxsize
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else:
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return -(sys.maxsize)
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base = 10 * base + (ord(Str[i]) - ord('0'))
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i += 1
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return base * sign
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inputs = tf.keras.Input(shape=(xatoi(sys.argv[1]),), 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 / randrange(255), x_test / randrange(255)
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model = tf.keras.models.Sequential([
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tf.keras.layers.Flatten(input_shape=(randrange(255), randrange(255))),
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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_train, y_train, epochs=10)
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outputs = tf.keras.layers.Dense(4, activation='softmax', name='predictions')((x_train,y_train))
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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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