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Cresson Remi authored8cc0e197
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# -*- coding: utf-8 -*-
# ==========================================================================
#
# Copyright 2018-2019 Remi Cresson (IRSTEA)
# Copyright 2020 Remi Cresson (INRAE)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# ==========================================================================*/
import argparse
from tricks import create_savedmodel
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
parser = argparse.ArgumentParser()
parser.add_argument("--nclasses", type=int, default=8, help="number of classes")
parser.add_argument("--outdir", help="Output directory for SavedModel", required=True)
params = parser.parse_args()
def my_model(x):
# input patches: 16x16x4
conv1 = tf.compat.v1.layers.conv2d(inputs=x, filters=16, kernel_size=[5, 5], padding="valid",
activation=tf.nn.relu) # out size: 12x12x16
pool1 = tf.compat.v1.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) # out: 6x6x16
conv2 = tf.compat.v1.layers.conv2d(inputs=pool1, filters=16, kernel_size=[3, 3], padding="valid",
activation=tf.nn.relu) # out size: 4x4x16
pool2 = tf.compat.v1.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) # out: 2x2x16
conv3 = tf.compat.v1.layers.conv2d(inputs=pool2, filters=32, kernel_size=[2, 2], padding="valid",
activation=tf.nn.relu) # out size: 1x1x32
# Features
features = tf.reshape(conv3, shape=[-1, 32], name="features")
# Neurons for classes
estimated = tf.compat.v1.layers.dense(inputs=features, units=params.nclasses, activation=None)
estimated_label = tf.argmax(estimated, 1, name="prediction")
return estimated, estimated_label
# Create the TensorFlow graph
with tf.compat.v1.Graph().as_default():
# Placeholders
x = tf.compat.v1.placeholder(tf.float32, [None, None, None, 4], name="x")
y = tf.compat.v1.placeholder(tf.int32, [None, None, None, 1], name="y")
lr = tf.compat.v1.placeholder_with_default(tf.constant(0.0002, dtype=tf.float32, shape=[]), shape=[], name="lr")
# Output
y_estimated, y_label = my_model(x)
# Loss function
cost = tf.compat.v1.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]),
logits=tf.reshape(y_estimated, [-1, params.nclasses]))
# Optimizer
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=lr, name="optimizer").minimize(cost)
# Initializer, saver, session
init = tf.compat.v1.global_variables_initializer()
saver = tf.compat.v1.train.Saver(max_to_keep=20)
sess = tf.compat.v1.Session()
sess.run(init)
# Create a SavedModel
create_savedmodel(sess, ["x:0", "y:0"], ["features:0", "prediction:0"], params.outdir)