wehub-resource-sync
79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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A small resnet-like network for quick testing.
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"""
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import tensorflow as tf
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def identity_block(input_tensor):
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"""
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Identity block with no shortcut convolution
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"""
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y = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), padding="same")(
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input_tensor
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)
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y = tf.keras.layers.ReLU()(y)
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y = tf.keras.layers.Conv2D(filters=24, kernel_size=(3, 3), padding="same")(y)
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out = tf.keras.layers.Add()([y, input_tensor])
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out = tf.keras.layers.ReLU()(out)
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return out
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def identity_block_short_conv(input_tensor):
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"""
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Identity block with shortcut convolution
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"""
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y = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), padding="same")(
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input_tensor
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)
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y = tf.keras.layers.ReLU()(y)
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y = tf.keras.layers.Conv2D(
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filters=24, kernel_size=(3, 3), strides=(2, 2), padding="same"
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)(y)
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ds_input = tf.keras.layers.Conv2D(
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filters=24, kernel_size=(3, 3), strides=(2, 2), padding="same"
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)(input_tensor)
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out = tf.keras.layers.Add()([y, ds_input])
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out = tf.keras.layers.ReLU()(out)
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return out
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def model():
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"""
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Dummy network with resnet-like architecture.
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"""
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input_img = tf.keras.layers.Input(shape=(32, 32, 3))
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x = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3))(input_img)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Conv2D(filters=24, kernel_size=(3, 3))(x)
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x = tf.keras.layers.ReLU()(x)
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x = identity_block(x)
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x = identity_block_short_conv(x)
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x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
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x = tf.keras.layers.Flatten()(x)
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x = tf.keras.layers.Dense(100)(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Dense(10)(x)
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return tf.keras.Model(input_img, x, name="Dummy_Model")
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def optimizer(lr=0.001):
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return tf.keras.optimizers.Adam(learning_rate=lr)
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