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architecture.py
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architecture.py
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import tensorflow as tf
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
import tensorflow.keras as keras
import tensorflow_probability as tfp
import numpy as np
import os
import argparse
import problem
def get_model(config):
l = tf.keras.layers
image = l.Input(problem.IMAGE_SHAPE + [1], name='image')
max_pool = l.MaxPooling2D((2, 2), padding='same')
probabilities = tf.keras.Sequential(
[
l.Conv2D(20, kernel_size=5, padding='same', activation=tf.nn.relu),
max_pool,
l.Conv2D(50, kernel_size=5, padding='same', activation=tf.nn.relu),
max_pool,
l.Flatten(),
l.Dense(100, activation=tf.nn.relu),
l.Dense(10, activation=tf.nn.softmax)
],
name='probabilities'
)(image)
return keras.models.Model(inputs=image, outputs=probabilities)
def compile_model(model, config):
model.compile(
optimizer=keras.optimizers.Adam(
learning_rate=config['learning_rate'],
beta_1=0.9,
beta_2=0.999,
clipvalue=config['gradient_clipvalue'],
),
loss=dict(
probabilities=tf.keras.losses.CategoricalCrossentropy(),
),
metrics=dict(
probabilities=[
tf.keras.metrics.CategoricalAccuracy()
],
),
target_tensors=dict(
probabilities=keras.layers.Input([1]),
),
# run_eagerly=True,
)