# Copyright 2024 NASA
+#
+# 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
+#
+# https://www.apache.org/licenses/LICENSE-2.0
+#
+# 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.
+
+
+
+
+
+
+
+
+Rice mapping in Bhutan with U-Net using high resolution satellite imagery
+
+
+
+
+  Run in Colab
+ |
+
+  View on GitHub
+ |
This notebook is also available in this github repo: https://github.com/SERVIR/servir-aces. Navigate to the notebooks folder.
Setup environment
+
+
+
+from google.colab import drive
+drive.mount("/content/drive")
+
+Mounted at /content/drive
+
+
+!pip install servir-aces
+
+Collecting servir-aces
+ Downloading servir_aces-0.0.14-py2.py3-none-any.whl (32 kB)
+Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from servir-aces) (1.25.2)
+Requirement already satisfied: tensorflow>=2.9.3 in /usr/local/lib/python3.10/dist-packages (from servir-aces) (2.15.0)
+Requirement already satisfied: earthengine-api in /usr/local/lib/python3.10/dist-packages (from servir-aces) (0.1.399)
+Collecting python-dotenv>=1.0.0 (from servir-aces)
+ Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)
+Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from servir-aces) (3.7.1)
+Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (1.4.0)
+Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (1.6.3)
+Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (24.3.25)
+Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (0.5.4)
+Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (0.2.0)
+Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (3.9.0)
+Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (18.1.1)
+Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (0.2.0)
+Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (3.3.0)
+Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (24.0)
+Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (3.20.3)
+Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (67.7.2)
+Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (1.16.0)
+Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (2.4.0)
+Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (4.11.0)
+Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (1.14.1)
+Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (0.36.0)
+Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (1.62.2)
+Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (2.15.2)
+Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (2.15.0)
+Requirement already satisfied: keras<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (2.15.0)
+Requirement already satisfied: google-cloud-storage in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (2.8.0)
+Requirement already satisfied: google-api-python-client>=1.12.1 in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (2.84.0)
+Requirement already satisfied: google-auth>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (2.27.0)
+Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (0.1.1)
+Requirement already satisfied: httplib2<1dev,>=0.9.2 in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (0.22.0)
+Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (2.31.0)
+Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (1.2.1)
+Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (0.12.1)
+Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (4.51.0)
+Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (1.4.5)
+Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (9.4.0)
+Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (3.1.2)
+Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (2.8.2)
+Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow>=2.9.3->servir-aces) (0.43.0)
+Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client>=1.12.1->earthengine-api->servir-aces) (2.11.1)
+Requirement already satisfied: uritemplate<5,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client>=1.12.1->earthengine-api->servir-aces) (4.1.1)
+Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth>=1.4.1->earthengine-api->servir-aces) (5.3.3)
+Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth>=1.4.1->earthengine-api->servir-aces) (0.4.0)
+Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth>=1.4.1->earthengine-api->servir-aces) (4.9)
+Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (1.2.0)
+Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (3.6)
+Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (0.7.2)
+Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (3.0.2)
+Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->earthengine-api->servir-aces) (3.3.2)
+Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->earthengine-api->servir-aces) (3.7)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->earthengine-api->servir-aces) (2.0.7)
+Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->earthengine-api->servir-aces) (2024.2.2)
+Requirement already satisfied: google-cloud-core<3.0dev,>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from google-cloud-storage->earthengine-api->servir-aces) (2.3.3)
+Requirement already satisfied: google-resumable-media>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from google-cloud-storage->earthengine-api->servir-aces) (2.7.0)
+Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.12.1->earthengine-api->servir-aces) (1.63.0)
+Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (1.3.1)
+Requirement already satisfied: google-crc32c<2.0dev,>=1.0 in /usr/local/lib/python3.10/dist-packages (from google-resumable-media>=2.3.2->google-cloud-storage->earthengine-api->servir-aces) (1.5.0)
+Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=1.4.1->earthengine-api->servir-aces) (0.6.0)
+Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (2.1.5)
+Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (3.2.2)
+Installing collected packages: python-dotenv, servir-aces
+Successfully installed python-dotenv-1.0.1 servir-aces-0.0.14
+
+
+!git clone https://github.com/SERVIR/servir-aces
+
+Cloning into 'servir-aces'...
+remote: Enumerating objects: 740, done.
+remote: Counting objects: 100% (116/116), done.
+remote: Compressing objects: 100% (78/78), done.
+remote: Total 740 (delta 46), reused 68 (delta 38), pack-reused 624
+Receiving objects: 100% (740/740), 5.07 MiB | 16.12 MiB/s, done.
+Resolving deltas: 100% (468/468), done.Download datasets
+For this chapter, we have already prepared and exported the training datasets. They can be found at the google cloud storage and we will use gsutil to get the dataset in our workspace. The dataset has training, testing, and validation subdirectory. Let’s start by downloading these datasets in our workspace.
If you’re looking to produce your own datasets, you can follow this notebook which was used to produce these training, testing, and validation datasets provided in this notebook.
+
+
+
+!mkdir /content/datasets
+
+
+!gsutil -m cp -r gs://dl-book/chapter-1/* /content/datasets
+
+Copying gs://dl-book/chapter-1/dnn_planet_wo_indices/training/training.tfrecord.gz...
+/ [0 files][ 0.0 B/352.3 KiB] Copying gs://dl-book/chapter-1/.DS_Store...
+/ [0 files][ 0.0 B/358.3 KiB] Copying gs://dl-book/chapter-1/dnn_planet_wo_indices/testing/testing.tfrecord.gz...
+/ [0 files][ 0.0 B/410.4 KiB] Copying gs://dl-book/chapter-1/images/image_202100000.tfrecord.gz...
+/ [0 files][ 0.0 B/ 50.8 MiB] Copying gs://dl-book/chapter-1/images/image_202100003.tfrecord.gz...
+/ [0 files][ 0.0 B/ 96.7 MiB] Copying gs://dl-book/chapter-1/images/image_202100001.tfrecord.gz...
+/ [0 files][ 0.0 B/142.5 MiB] Copying gs://dl-book/chapter-1/images/image_202100002.tfrecord.gz...
+/ [0 files][ 0.0 B/189.5 MiB] Copying gs://dl-book/chapter-1/dnn_planet_wo_indices/validation/validation.tfrecord.gz...
+/ [0 files][ 0.0 B/189.6 MiB] Copying gs://dl-book/chapter-1/images/image_202100004.tfrecord.gz...
+Copying gs://dl-book/chapter-1/images/image_202100005.tfrecord.gz...
+Copying gs://dl-book/chapter-1/images/image_2021mixer.json...
+Copying gs://dl-book/chapter-1/prediction/prediction_dnn_v1.TFRecord...
+==> NOTE: You are downloading one or more large file(s), which would
+run significantly faster if you enabled sliced object downloads. This
+feature is enabled by default but requires that compiled crcmod be
+installed (see "gsutil help crcmod").
+
+Copying gs://dl-book/chapter-1/prediction/prediction_unet_v1.TFRecord...
+Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00000-of-00008.tfrecord.gz...
+Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00001-of-00008.tfrecord.gz...
+Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00002-of-00008.tfrecord.gz...
+Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00003-of-00008.tfrecord.gz...
+Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00004-of-00008.tfrecord.gz...
+Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00005-of-00008.tfrecord.gz...
+Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00006-of-00008.tfrecord.gz...
+Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00007-of-00008.tfrecord.gz...
+Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00000-of-00038.tfrecord.gz...
+Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00001-of-00038.tfrecord.gz...
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+Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00010-of-00038.tfrecord.gz...
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+Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00019-of-00038.tfrecord.gz...
+Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00020-of-00038.tfrecord.gz...
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+Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00002-of-00038.tfrecord.gz...
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+Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00005-of-00038.tfrecord.gz...
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+Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00037-of-00038.tfrecord.gz...Setup config file variables
+Now the repo is downloaded. We will create an environment file file to place point to our training data and customize parameters for the model. To do this, we make a copy of the .env.example file provided.
Under the hood, all the configuration provided via the environment file are parsed as a config object and can be accessed programatically.
+Note current version does not expose all the model intracacies through the environment file but future version may include those depending on the need.
+
+
+
+!cp servir-aces/.env.example servir-aces/config.envOkay, now we have the config.env file, we will use this to provide our environments and parameters.
Note there are several parameters that can be changed. Let’s start by changing the BASEDIR and OUTPUT_DIR as below.
BASEDIR = "/content/"
+OUTPUT_DIR = "/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output"We will start by training a U-Net model using the dl-book/chapter-1/unet_256x256_planet_wo_indices dataset inside the dataset folder for this exercise. Let’s go ahead and change our DATADIR in the config.env file as below.
DATADIR = "datasets/unet_256x256_planet_wo_indices"These datasets have RGBN from Planetscope mosiac. Since we are trying to map the rice fields, we use growing season and pre-growing season information. Thus, we have 8 optical bands, namely red_before, green_before, blue_before, nir_before, red_during, green_during, blue_during, and nir_during. In adidition, you can use USE_ELEVATION and USE_S1 config to include the topographic and radar information. Since this datasets have toppgraphic and radar features, so we won’t be settting these config values. Similarly, these datasets are tiled to 256x256 pixels, so let’s also change that.
# For model training, USE_ELEVATION extends FEATURES with "elevation" & "slope"
+# USE_S1 extends FEATURES with "vv_asc_before", "vh_asc_before", "vv_asc_during", "vh_asc_during",
+# "vv_desc_before", "vh_desc_before", "vv_desc_during", "vh_desc_during"
+# In case these are not useful and you have other bands in your training data, you can do set
+# USE_ELEVATION and USE_S1 to False and update FEATURES to include needed bands
+USE_ELEVATION = False
+USE_S1 = False
+
+PATCH_SHAPE = (256, 256)Next, we need to calculate the size of the traiing, testing and validation dataset. For this, we know our size before hand. But aces also provides handful of functions that we can use to calculate this. See this notebook to learn more about how to do it. We will also change the BATCH_SIZE to 32; if you have larger memory available, you can increase the BATCH_SIZE. You can run for longer EPOCHS by changing the EPOCHS paramter; we will keep it to 5 for now.
# Sizes of the training and evaluation datasets.
+TRAIN_SIZE = 8531
+TEST_SIZE = 1222
+VAL_SIZE = 2404
+BATCH_SIZE = 32
+EPOCHS = 30Update the config file programtically
+We can also make a dictionary so we can change these config settings programatically.
+
+
+
+BASEDIR = "/content/" # @param {type:"string"}
+OUTPUT_DIR = "/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output" # @param {type:"string"}
+DATADIR = "datasets/unet_256x256_planet_wo_indices" # @param {type:"string"}
+# PATCH_SHAPE, USE_ELEVATION, USE_S1, TRAIN_SIZE, TEST_SIZE, VAL_SIZE
+# BATCH_SIZE, EPOCHS are converted to their appropriate type.
+USE_ELEVATION = "False" # @param {type:"string"}
+USE_S1 = "False" # @param {type:"string"}
+PATCH_SHAPE = "(256, 256)" # @param {type:"string"}
+TRAIN_SIZE = "8531" # @param {type:"string"}
+TEST_SIZE = "1222" # @param {type:"string"}
+VAL_SIZE = "2404" # @param {type:"string"}
+BATCH_SIZE = "32" # @param {type:"string"}
+EPOCHS = "30" # @param {type:"string"}
+MODEL_DIR_NAME = "unet_v1" # @param {type:"string"}
+
+
+unet_config_settings = {
+ "BASEDIR" : BASEDIR,
+ "OUTPUT_DIR": OUTPUT_DIR,
+ "DATADIR": DATADIR,
+ "USE_ELEVATION": USE_ELEVATION,
+ "USE_S1": USE_S1,
+ "PATCH_SHAPE": PATCH_SHAPE,
+ "TRAIN_SIZE": TRAIN_SIZE,
+ "TEST_SIZE": TEST_SIZE,
+ "VAL_SIZE": VAL_SIZE,
+ "BATCH_SIZE": BATCH_SIZE,
+ "EPOCHS": EPOCHS,
+ "MODEL_DIR_NAME": MODEL_DIR_NAME,
+}
+
+
+import dotenv
+
+config_file = "servir-aces/config.env"
+
+for config_key in unet_config_settings:
+ dotenv.set_key(dotenv_path=config_file,
+ key_to_set=config_key,
+ value_to_set=unet_config_settings[config_key]
+ )U-Net Model
+Load config file variables
+
+
+
+from aces import Config, DataProcessor, ModelTrainer, EEUtilsLet’s load our config file through the Config class.
+
+
+unet_config = Config(config_file=config_file)
+
+BASEDIR: /content
+DATADIR: /content/datasets/unet_256x256_planet_wo_indices
+using features: ['red_before', 'green_before', 'blue_before', 'nir_before', 'red_during', 'green_during', 'blue_during', 'nir_during']
+using labels: ['class']Most of the config in the config.env is now available via the config instance. Let’s check few of them here.
+
+
+unet_config.TRAINING_DIR, unet_config.OUTPUT_DIR, unet_config.BATCH_SIZE, unet_config.TRAIN_SIZE
+
+(PosixPath('/content/datasets/unet_256x256_planet_wo_indices/training'),
+ PosixPath('/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output'),
+ 32,
+ 8531)Load ModelTrainer class
+Next, let’s make an instance of the ModelTrainer object. The ModelTrainer class provides various tools for training, buidling, compiling, and running specified deep learning models.
+
+
+unet_model_trainer = ModelTrainer(unet_config, seed=42)
+
+Using seed: 42Train and Save U-Net model
+ModelTrainer class provides various functionality. We will use train_model function that helps to train the model using the provided configuration settings.
This method performs the following steps: - Configures memory growth for TensorFlow. - Creates TensorFlow datasets for training, testing, and validation. - Builds and compiles the model. - Prepares the output directory for saving models and results. - Starts the training process. - Evaluates and prints validation metrics. - Saves training parameters, plots, and models.
+
+
+
+unet_model_trainer.train_model()
+
+****************************************************************************
+****************************** Clear Session... ****************************
+****************************************************************************
+****************************** Configure memory growth... ************************
+ > Found 1 GPUs
+****************************************************************************
+****************************** creating datasets... ************************
+Loading dataset from /content/datasets/unet_256x256_planet_wo_indices/training/*
+randomly transforming data
+Loading dataset from /content/datasets/unet_256x256_planet_wo_indices/validation/*
+Loading dataset from /content/datasets/unet_256x256_planet_wo_indices/testing/*
+Printing dataset info:
+Training
+inputs: float32 (32, 256, 256, 8)
+tf.Tensor(
+[[[[0.073075 0.063275 0.0411 ... 0.050625 0.0274 0.23925 ]
+ [0.084775 0.067375 0.047025 ... 0.057675 0.032075 0.242375]
+ [0.083625 0.068575 0.045075 ... 0.059275 0.0332 0.2409 ]
+ ...
+ [0.0702 0.06825 0.04495 ... 0.055025 0.028325 0.26305 ]
+ [0.064475 0.066 0.043575 ... 0.0524 0.027075 0.26705 ]
+ [0.0676 0.06355 0.04535 ... 0.05375 0.02875 0.263275]]
+
+ [[0.071475 0.062225 0.0388 ... 0.0496 0.025375 0.24155 ]
+ [0.07815 0.065025 0.044225 ... 0.0545 0.02905 0.24175 ]
+ [0.086025 0.069125 0.046175 ... 0.05855 0.0326 0.2355 ]
+ ...
+ [0.060775 0.0627 0.041875 ... 0.051575 0.029725 0.267475]
+ [0.061375 0.06225 0.04225 ... 0.0513 0.02685 0.268375]
+ [0.06845 0.064075 0.043925 ... 0.052925 0.028575 0.267975]]
+
+ [[0.0677 0.0605 0.038625 ... 0.04835 0.024825 0.236075]
+ [0.078375 0.0629 0.04215 ... 0.0524 0.02855 0.237375]
+ [0.0857 0.065725 0.04635 ... 0.05705 0.030975 0.235375]
+ ...
+ [0.07 0.062775 0.04485 ... 0.053425 0.0292 0.27015 ]
+ [0.0607 0.060675 0.041175 ... 0.053075 0.026275 0.27025 ]
+ [0.068 0.0667 0.045375 ... 0.055475 0.029375 0.262725]]
+
+ ...
+
+ [[0.083525 0.06785 0.044125 ... 0.06365 0.0331 0.234825]
+ [0.097825 0.07235 0.047925 ... 0.06675 0.03365 0.2363 ]
+ [0.1092 0.082125 0.05385 ... 0.072125 0.036225 0.2486 ]
+ ...
+ [0.08935 0.088725 0.067575 ... 0.079675 0.042425 0.38085 ]
+ [0.093725 0.0875 0.06355 ... 0.07565 0.04185 0.344525]
+ [0.0937 0.089675 0.066775 ... 0.07465 0.043025 0.330925]]
+
+ [[0.0893 0.0732 0.04715 ... 0.065 0.0351 0.233525]
+ [0.091325 0.073425 0.047475 ... 0.0653 0.032675 0.238325]
+ [0.096775 0.07645 0.051625 ... 0.06875 0.0344 0.252825]
+ ...
+ [0.0836 0.084875 0.061975 ... 0.07825 0.042875 0.38785 ]
+ [0.08865 0.083825 0.060675 ... 0.0765 0.042525 0.3522 ]
+ [0.0909 0.084475 0.061975 ... 0.0769 0.043275 0.342625]]
+
+ [[0.092075 0.078 0.050925 ... 0.06565 0.03555 0.235275]
+ [0.0805 0.0705 0.043325 ... 0.063925 0.03215 0.243875]
+ [0.086925 0.074025 0.0495 ... 0.067475 0.03345 0.26095 ]
+ ...
+ [0.081075 0.078725 0.056425 ... 0.07505 0.0398 0.37805 ]
+ [0.0865 0.079375 0.05845 ... 0.076175 0.0439 0.3619 ]
+ [0.0886 0.077775 0.057725 ... 0.076175 0.042825 0.3439 ]]]
+
+
+ [[[0.076525 0.0703 0.04595 ... 0.055225 0.028025 0.25075 ]
+ [0.072025 0.0658 0.0446 ... 0.05555 0.02795 0.24755 ]
+ [0.0669 0.06225 0.038125 ... 0.05245 0.027125 0.241425]
+ ...
+ [0.054175 0.050575 0.029475 ... 0.04845 0.022375 0.23045 ]
+ [0.05465 0.052375 0.031125 ... 0.04935 0.024375 0.2282 ]
+ [0.052525 0.052725 0.029275 ... 0.048325 0.02325 0.229475]]
+
+ [[0.0784 0.065975 0.0441 ... 0.0594 0.031425 0.241175]
+ [0.075475 0.066225 0.044975 ... 0.05505 0.02915 0.2405 ]
+ [0.073375 0.063225 0.044475 ... 0.05435 0.029375 0.243575]
+ ...
+ [0.047325 0.05035 0.027125 ... 0.04535 0.022275 0.2235 ]
+ [0.046475 0.051075 0.026425 ... 0.047025 0.021025 0.2348 ]
+ [0.04295 0.050275 0.02575 ... 0.044525 0.01955 0.240875]]
+
+ [[0.065825 0.0619 0.04045 ... 0.053225 0.026425 0.236775]
+ [0.07745 0.062725 0.040725 ... 0.0573 0.030725 0.2439 ]
+ [0.075525 0.063775 0.0434 ... 0.05595 0.030125 0.25005 ]
+ ...
+ [0.046675 0.048325 0.02605 ... 0.0475 0.0219 0.23165 ]
+ [0.046825 0.04955 0.026425 ... 0.0471 0.02055 0.243125]
+ [0.04435 0.0498 0.0253 ... 0.04675 0.020775 0.239925]]
+
+ ...
+
+ [[0.028025 0.041275 0.01945 ... 0.039375 0.015675 0.22205 ]
+ [0.0245 0.040675 0.018025 ... 0.039575 0.016475 0.2187 ]
+ [0.02185 0.03435 0.01665 ... 0.034025 0.015 0.20335 ]
+ ...
+ [0.1155 0.09395 0.0714 ... 0.058625 0.0275 0.335675]
+ [0.117225 0.09435 0.0699 ... 0.05885 0.028175 0.34795 ]
+ [0.1168 0.093275 0.06865 ... 0.0585 0.02895 0.353275]]
+
+ [[0.032025 0.04075 0.020675 ... 0.04025 0.015525 0.2328 ]
+ [0.024525 0.038175 0.018025 ... 0.03785 0.015075 0.21255 ]
+ [0.0227 0.03625 0.016425 ... 0.035 0.015075 0.204675]
+ ...
+ [0.11625 0.093825 0.071275 ... 0.058625 0.02685 0.34765 ]
+ [0.115325 0.092175 0.06915 ... 0.05855 0.02745 0.3572 ]
+ [0.1143 0.091225 0.067325 ... 0.05835 0.028925 0.357825]]
+
+ [[0.033325 0.04015 0.0212 ... 0.037875 0.015575 0.220525]
+ [0.027225 0.038525 0.01925 ... 0.03625 0.014825 0.207775]
+ [0.02625 0.03785 0.01885 ... 0.035675 0.015175 0.209825]
+ ...
+ [0.1132 0.09225 0.0699 ... 0.057875 0.027175 0.352875]
+ [0.1116 0.090575 0.0685 ... 0.0585 0.027325 0.36045 ]
+ [0.110325 0.089725 0.06665 ... 0.059425 0.02975 0.35485 ]]]
+
+
+ [[[0.076325 0.0714 0.0511 ... 0.05685 0.027375 0.3285 ]
+ [0.078825 0.066725 0.044825 ... 0.05665 0.03155 0.3196 ]
+ [0.1038 0.0806 0.060575 ... 0.07545 0.048225 0.2805 ]
+ ...
+ [0.02885 0.040825 0.022125 ... 0.037725 0.016275 0.17815 ]
+ [0.0286 0.0422 0.02355 ... 0.039625 0.016675 0.191225]
+ [0.02775 0.04375 0.022175 ... 0.043325 0.0181 0.203775]]
+
+ [[0.06785 0.062075 0.04025 ... 0.04975 0.026175 0.31845 ]
+ [0.07785 0.06515 0.041575 ... 0.055275 0.033675 0.29555 ]
+ [0.099375 0.0823 0.062 ... 0.076125 0.047775 0.27305 ]
+ ...
+ [0.026425 0.040625 0.021825 ... 0.037175 0.0163 0.180075]
+ [0.0283 0.04245 0.02205 ... 0.04045 0.017175 0.192025]
+ [0.02925 0.0436 0.022975 ... 0.043725 0.0179 0.20435 ]]
+
+ [[0.064725 0.0621 0.0413 ... 0.05105 0.02655 0.30515 ]
+ [0.08075 0.067625 0.0489 ... 0.0599 0.033625 0.28425 ]
+ [0.1018 0.078725 0.060025 ... 0.0735 0.043225 0.2772 ]
+ ...
+ [0.0277 0.0412 0.020975 ... 0.03765 0.01625 0.184425]
+ [0.02835 0.043125 0.021675 ... 0.040175 0.017375 0.19335 ]
+ [0.030575 0.043325 0.023375 ... 0.04225 0.0173 0.200575]]
+
+ ...
+
+ [[0.06545 0.054525 0.034075 ... 0.05745 0.028325 0.244075]
+ [0.06275 0.053075 0.03125 ... 0.055625 0.027675 0.247475]
+ [0.060875 0.05235 0.030725 ... 0.053875 0.026575 0.247275]
+ ...
+ [0.04905 0.0508 0.031375 ... 0.039275 0.018625 0.184025]
+ [0.047775 0.04855 0.03135 ... 0.038075 0.017725 0.173025]
+ [0.048475 0.052025 0.0336 ... 0.0377 0.018625 0.172875]]
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+ [0.09965 0.082725 0.06805 ... 0.067325 0.05815 0.27725 ]
+ [0.0889 0.0679 0.0468 ... 0.0563 0.034875 0.29495 ]
+ [0.07205 0.059575 0.04125 ... 0.05235 0.03185 0.3116 ]]
+
+ [[0.0768 0.06205 0.039975 ... 0.058175 0.0334 0.197525]
+ [0.0797 0.0638 0.041675 ... 0.060425 0.035925 0.1993 ]
+ [0.08345 0.063725 0.04135 ... 0.0606 0.03585 0.2044 ]
+ ...
+ [0.110425 0.089975 0.071475 ... 0.083225 0.07175 0.261625]
+ [0.0995 0.076725 0.053175 ... 0.060975 0.043725 0.29315 ]
+ [0.07945 0.06385 0.0462 ... 0.059675 0.038375 0.32095 ]]
+
+ [[0.074075 0.0615 0.0395 ... 0.0591 0.03185 0.200825]
+ [0.0771 0.06265 0.040775 ... 0.059825 0.033975 0.204725]
+ [0.0835 0.063125 0.0417 ... 0.059825 0.034325 0.2 ]
+ ...
+ [0.118575 0.0944 0.070325 ... 0.09795 0.078 0.272 ]
+ [0.11975 0.0899 0.063575 ... 0.077975 0.05495 0.306325]
+ [0.0861 0.068475 0.049775 ... 0.063225 0.039625 0.3163 ]]]], shape=(1, 256, 256, 8), dtype=float32)
+outputs: float32 (1, 256, 256, 5)
+tf.Tensor(
+[[[[1. 0. 0. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ [0. 1. 0. 0. 0.]
+ ...
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]]
+
+ [[0. 0. 1. 0. 0.]
+ [0. 0. 0. 1. 0.]
+ [0. 0. 0. 1. 0.]
+ ...
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ ...
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]]
+
+ ...
+
+ [[1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ ...
+ [0. 0. 1. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]]
+
+ [[1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ ...
+ [0. 0. 0. 1. 0.]
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]]
+
+ [[1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ ...
+ [0. 0. 0. 1. 0.]
+ [0. 0. 1. 0. 0.]
+ [1. 0. 0. 0. 0.]]]], shape=(1, 256, 256, 5), dtype=float32)
+Validation
+inputs: float32 (1, 256, 256, 8)
+tf.Tensor(
+[[[[0.053275 0.043025 0.0284 ... 0.042575 0.01925 0.2313 ]
+ [0.0535 0.04265 0.0293 ... 0.043975 0.0191 0.246425]
+ [0.049125 0.042675 0.027125 ... 0.042275 0.019325 0.228225]
+ ...
+ [0.0724 0.064525 0.044325 ... 0.0504 0.0264 0.202325]
+ [0.07395 0.0651 0.04495 ... 0.05235 0.02625 0.211175]
+ [0.075975 0.0647 0.04615 ... 0.0523 0.027625 0.2079 ]]
+
+ [[0.053025 0.042325 0.02895 ... 0.041625 0.018475 0.239625]
+ [0.051225 0.0413 0.029 ... 0.042 0.018375 0.238775]
+ [0.04785 0.04345 0.02785 ... 0.042625 0.019825 0.21835 ]
+ ...
+ [0.067 0.059125 0.042375 ... 0.049375 0.023475 0.18365 ]
+ [0.0679 0.06215 0.042125 ... 0.050825 0.0246 0.197125]
+ [0.066575 0.062775 0.041925 ... 0.049875 0.0247 0.199775]]
+
+ [[0.04975 0.03945 0.0265 ... 0.040925 0.01785 0.243675]
+ [0.050625 0.040725 0.027925 ... 0.040825 0.018625 0.236075]
+ [0.0546 0.04545 0.029725 ... 0.043575 0.021075 0.20885 ]
+ ...
+ [0.069075 0.0611 0.0435 ... 0.050075 0.02435 0.186325]
+ [0.07345 0.063225 0.0452 ... 0.052325 0.02595 0.19745 ]
+ [0.068175 0.06035 0.04155 ... 0.04985 0.023925 0.1912 ]]
+
+ ...
+
+ [[0.064425 0.062275 0.037175 ... 0.0576 0.027975 0.265325]
+ [0.058075 0.059925 0.03495 ... 0.05475 0.02585 0.26375 ]
+ [0.040675 0.053675 0.028975 ... 0.0482 0.02065 0.250575]
+ ...
+ [0.0937 0.09025 0.072 ... 0.0486 0.02375 0.2789 ]
+ [0.094125 0.091525 0.072925 ... 0.04795 0.02335 0.273275]
+ [0.09135 0.08855 0.067875 ... 0.04985 0.023425 0.282475]]
+
+ [[0.063175 0.05715 0.03525 ... 0.054475 0.0265 0.2553 ]
+ [0.0581 0.0556 0.032875 ... 0.0511 0.0242 0.246625]
+ [0.0396 0.0509 0.027975 ... 0.0464 0.020075 0.23445 ]
+ ...
+ [0.09535 0.0905 0.076275 ... 0.048725 0.0235 0.289175]
+ [0.093725 0.09015 0.0717 ... 0.048325 0.02345 0.279575]
+ [0.09145 0.088125 0.068475 ... 0.0493 0.023075 0.290275]]
+
+ [[0.04605 0.05285 0.0288 ... 0.048925 0.021625 0.2413 ]
+ [0.03955 0.051625 0.028325 ... 0.046975 0.020875 0.2319 ]
+ [0.0431 0.052225 0.03135 ... 0.042275 0.02 0.221325]
+ ...
+ [0.099075 0.085075 0.06545 ... 0.051925 0.02575 0.298475]
+ [0.100175 0.08775 0.0678 ... 0.05005 0.024175 0.28905 ]
+ [0.09685 0.0912 0.07425 ... 0.049975 0.023375 0.290425]]]], shape=(1, 256, 256, 8), dtype=float32)
+outputs: float32 (1, 256, 256, 5)
+tf.Tensor(
+[[[[0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ ...
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]]
+
+ [[0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ ...
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]]
+
+ [[0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ ...
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]
+ [1. 0. 0. 0. 0.]]
+
+ ...
+
+ [[0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ ...
+ [0. 1. 0. 0. 0.]
+ [0. 1. 0. 0. 0.]
+ [0. 1. 0. 0. 0.]]
+
+ [[0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ ...
+ [0. 1. 0. 0. 0.]
+ [0. 1. 0. 0. 0.]
+ [0. 1. 0. 0. 0.]]
+
+ [[0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ [0. 0. 1. 0. 0.]
+ ...
+ [0. 1. 0. 0. 0.]
+ [0. 1. 0. 0. 0.]
+ [0. 1. 0. 0. 0.]]]], shape=(1, 256, 256, 5), dtype=float32)
+****************************************************************************
+************************ building and compiling model... *******************
+DERIVE_FEATURES: False
+Model: "unet"
+__________________________________________________________________________________________________
+ Layer (type) Output Shape Param # Connected to
+==================================================================================================
+ input_1 (InputLayer) [(None, None, None, 8)] 0 []
+
+ conv2d (Conv2D) (None, None, None, 32) 2336 ['input_1[0][0]']
+
+ batch_normalization (Batch (None, None, None, 32) 128 ['conv2d[0][0]']
+ Normalization)
+
+ activation (Activation) (None, None, None, 32) 0 ['batch_normalization[0][0]']
+
+ activation_1 (Activation) (None, None, None, 32) 0 ['activation[0][0]']
+
+ separable_conv2d (Separabl (None, None, None, 64) 2400 ['activation_1[0][0]']
+ eConv2D)
+
+ batch_normalization_1 (Bat (None, None, None, 64) 256 ['separable_conv2d[0][0]']
+ chNormalization)
+
+ activation_2 (Activation) (None, None, None, 64) 0 ['batch_normalization_1[0][0]'
+ ]
+
+ separable_conv2d_1 (Separa (None, None, None, 64) 4736 ['activation_2[0][0]']
+ bleConv2D)
+
+ batch_normalization_2 (Bat (None, None, None, 64) 256 ['separable_conv2d_1[0][0]']
+ chNormalization)
+
+ max_pooling2d (MaxPooling2 (None, None, None, 64) 0 ['batch_normalization_2[0][0]'
+ D) ]
+
+ conv2d_1 (Conv2D) (None, None, None, 64) 2112 ['activation[0][0]']
+
+ add (Add) (None, None, None, 64) 0 ['max_pooling2d[0][0]',
+ 'conv2d_1[0][0]']
+
+ activation_3 (Activation) (None, None, None, 64) 0 ['add[0][0]']
+
+ separable_conv2d_2 (Separa (None, None, None, 128) 8896 ['activation_3[0][0]']
+ bleConv2D)
+
+ batch_normalization_3 (Bat (None, None, None, 128) 512 ['separable_conv2d_2[0][0]']
+ chNormalization)
+
+ activation_4 (Activation) (None, None, None, 128) 0 ['batch_normalization_3[0][0]'
+ ]
+
+ separable_conv2d_3 (Separa (None, None, None, 128) 17664 ['activation_4[0][0]']
+ bleConv2D)
+
+ batch_normalization_4 (Bat (None, None, None, 128) 512 ['separable_conv2d_3[0][0]']
+ chNormalization)
+
+ max_pooling2d_1 (MaxPoolin (None, None, None, 128) 0 ['batch_normalization_4[0][0]'
+ g2D) ]
+
+ conv2d_2 (Conv2D) (None, None, None, 128) 8320 ['add[0][0]']
+
+ add_1 (Add) (None, None, None, 128) 0 ['max_pooling2d_1[0][0]',
+ 'conv2d_2[0][0]']
+
+ activation_5 (Activation) (None, None, None, 128) 0 ['add_1[0][0]']
+
+ separable_conv2d_4 (Separa (None, None, None, 256) 34176 ['activation_5[0][0]']
+ bleConv2D)
+
+ batch_normalization_5 (Bat (None, None, None, 256) 1024 ['separable_conv2d_4[0][0]']
+ chNormalization)
+
+ activation_6 (Activation) (None, None, None, 256) 0 ['batch_normalization_5[0][0]'
+ ]
+
+ separable_conv2d_5 (Separa (None, None, None, 256) 68096 ['activation_6[0][0]']
+ bleConv2D)
+
+ batch_normalization_6 (Bat (None, None, None, 256) 1024 ['separable_conv2d_5[0][0]']
+ chNormalization)
+
+ max_pooling2d_2 (MaxPoolin (None, None, None, 256) 0 ['batch_normalization_6[0][0]'
+ g2D) ]
+
+ conv2d_3 (Conv2D) (None, None, None, 256) 33024 ['add_1[0][0]']
+
+ add_2 (Add) (None, None, None, 256) 0 ['max_pooling2d_2[0][0]',
+ 'conv2d_3[0][0]']
+
+ activation_7 (Activation) (None, None, None, 256) 0 ['add_2[0][0]']
+
+ conv2d_transpose (Conv2DTr (None, None, None, 256) 590080 ['activation_7[0][0]']
+ anspose)
+
+ batch_normalization_7 (Bat (None, None, None, 256) 1024 ['conv2d_transpose[0][0]']
+ chNormalization)
+
+ activation_8 (Activation) (None, None, None, 256) 0 ['batch_normalization_7[0][0]'
+ ]
+
+ conv2d_transpose_1 (Conv2D (None, None, None, 256) 590080 ['activation_8[0][0]']
+ Transpose)
+
+ batch_normalization_8 (Bat (None, None, None, 256) 1024 ['conv2d_transpose_1[0][0]']
+ chNormalization)
+
+ up_sampling2d_1 (UpSamplin (None, None, None, 256) 0 ['add_2[0][0]']
+ g2D)
+
+ up_sampling2d (UpSampling2 (None, None, None, 256) 0 ['batch_normalization_8[0][0]'
+ D) ]
+
+ conv2d_4 (Conv2D) (None, None, None, 256) 65792 ['up_sampling2d_1[0][0]']
+
+ add_3 (Add) (None, None, None, 256) 0 ['up_sampling2d[0][0]',
+ 'conv2d_4[0][0]']
+
+ activation_9 (Activation) (None, None, None, 256) 0 ['add_3[0][0]']
+
+ conv2d_transpose_2 (Conv2D (None, None, None, 128) 295040 ['activation_9[0][0]']
+ Transpose)
+
+ batch_normalization_9 (Bat (None, None, None, 128) 512 ['conv2d_transpose_2[0][0]']
+ chNormalization)
+
+ activation_10 (Activation) (None, None, None, 128) 0 ['batch_normalization_9[0][0]'
+ ]
+
+ conv2d_transpose_3 (Conv2D (None, None, None, 128) 147584 ['activation_10[0][0]']
+ Transpose)
+
+ batch_normalization_10 (Ba (None, None, None, 128) 512 ['conv2d_transpose_3[0][0]']
+ tchNormalization)
+
+ up_sampling2d_3 (UpSamplin (None, None, None, 256) 0 ['add_3[0][0]']
+ g2D)
+
+ up_sampling2d_2 (UpSamplin (None, None, None, 128) 0 ['batch_normalization_10[0][0]
+ g2D) ']
+
+ conv2d_5 (Conv2D) (None, None, None, 128) 32896 ['up_sampling2d_3[0][0]']
+
+ add_4 (Add) (None, None, None, 128) 0 ['up_sampling2d_2[0][0]',
+ 'conv2d_5[0][0]']
+
+ activation_11 (Activation) (None, None, None, 128) 0 ['add_4[0][0]']
+
+ conv2d_transpose_4 (Conv2D (None, None, None, 64) 73792 ['activation_11[0][0]']
+ Transpose)
+
+ batch_normalization_11 (Ba (None, None, None, 64) 256 ['conv2d_transpose_4[0][0]']
+ tchNormalization)
+
+ activation_12 (Activation) (None, None, None, 64) 0 ['batch_normalization_11[0][0]
+ ']
+
+ conv2d_transpose_5 (Conv2D (None, None, None, 64) 36928 ['activation_12[0][0]']
+ Transpose)
+
+ batch_normalization_12 (Ba (None, None, None, 64) 256 ['conv2d_transpose_5[0][0]']
+ tchNormalization)
+
+ up_sampling2d_5 (UpSamplin (None, None, None, 128) 0 ['add_4[0][0]']
+ g2D)
+
+ up_sampling2d_4 (UpSamplin (None, None, None, 64) 0 ['batch_normalization_12[0][0]
+ g2D) ']
+
+ conv2d_6 (Conv2D) (None, None, None, 64) 8256 ['up_sampling2d_5[0][0]']
+
+ add_5 (Add) (None, None, None, 64) 0 ['up_sampling2d_4[0][0]',
+ 'conv2d_6[0][0]']
+
+ activation_13 (Activation) (None, None, None, 64) 0 ['add_5[0][0]']
+
+ conv2d_transpose_6 (Conv2D (None, None, None, 32) 18464 ['activation_13[0][0]']
+ Transpose)
+
+ batch_normalization_13 (Ba (None, None, None, 32) 128 ['conv2d_transpose_6[0][0]']
+ tchNormalization)
+
+ activation_14 (Activation) (None, None, None, 32) 0 ['batch_normalization_13[0][0]
+ ']
+
+ conv2d_transpose_7 (Conv2D (None, None, None, 32) 9248 ['activation_14[0][0]']
+ Transpose)
+
+ batch_normalization_14 (Ba (None, None, None, 32) 128 ['conv2d_transpose_7[0][0]']
+ tchNormalization)
+
+ up_sampling2d_7 (UpSamplin (None, None, None, 64) 0 ['add_5[0][0]']
+ g2D)
+
+ up_sampling2d_6 (UpSamplin (None, None, None, 32) 0 ['batch_normalization_14[0][0]
+ g2D) ']
+
+ conv2d_7 (Conv2D) (None, None, None, 32) 2080 ['up_sampling2d_7[0][0]']
+
+ add_6 (Add) (None, None, None, 32) 0 ['up_sampling2d_6[0][0]',
+ 'conv2d_7[0][0]']
+
+ final_conv (Conv2D) (None, None, None, 5) 1445 ['add_6[0][0]']
+
+==================================================================================================
+Total params: 2060997 (7.86 MB)
+Trainable params: 2057221 (7.85 MB)
+Non-trainable params: 3776 (14.75 KB)
+__________________________________________________________________________________________________
+None
+****************************************************************************
+************************ preparing output directory... *********************
+> Saving models and results at /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1...
+****************************************************************************
+****************************** training model... ***************************
+Epoch 1/30
+266/266 [==============================] - ETA: 0s - loss: 0.9676 - precision: 0.7271 - recall: 0.5957 - categorical_accuracy: 0.6706 - one_hot_io_u: 0.4242
+Epoch 1: val_loss improved from inf to 2.63403, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 299s 971ms/step - loss: 0.9676 - precision: 0.7271 - recall: 0.5957 - categorical_accuracy: 0.6706 - one_hot_io_u: 0.4242 - val_loss: 2.6340 - val_precision: 0.3634 - val_recall: 0.3629 - val_categorical_accuracy: 0.3636 - val_one_hot_io_u: 0.0727
+Epoch 2/30
+266/266 [==============================] - ETA: 0s - loss: 0.6959 - precision: 0.8066 - recall: 0.6767 - categorical_accuracy: 0.7436 - one_hot_io_u: 0.5043
+Epoch 2: val_loss improved from 2.63403 to 1.23879, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 263s 958ms/step - loss: 0.6959 - precision: 0.8066 - recall: 0.6767 - categorical_accuracy: 0.7436 - one_hot_io_u: 0.5043 - val_loss: 1.2388 - val_precision: 0.5670 - val_recall: 0.4644 - val_categorical_accuracy: 0.5205 - val_one_hot_io_u: 0.2377
+Epoch 3/30
+266/266 [==============================] - ETA: 0s - loss: 0.6352 - precision: 0.8285 - recall: 0.7046 - categorical_accuracy: 0.7664 - one_hot_io_u: 0.5343
+Epoch 3: val_loss improved from 1.23879 to 0.62282, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 256s 966ms/step - loss: 0.6352 - precision: 0.8285 - recall: 0.7046 - categorical_accuracy: 0.7664 - one_hot_io_u: 0.5343 - val_loss: 0.6228 - val_precision: 0.8355 - val_recall: 0.7063 - val_categorical_accuracy: 0.7714 - val_one_hot_io_u: 0.5354
+Epoch 4/30
+266/266 [==============================] - ETA: 0s - loss: 0.5988 - precision: 0.8402 - recall: 0.7216 - categorical_accuracy: 0.7797 - one_hot_io_u: 0.5531
+Epoch 4: val_loss improved from 0.62282 to 0.60250, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 252s 949ms/step - loss: 0.5988 - precision: 0.8402 - recall: 0.7216 - categorical_accuracy: 0.7797 - one_hot_io_u: 0.5531 - val_loss: 0.6025 - val_precision: 0.8380 - val_recall: 0.7265 - val_categorical_accuracy: 0.7809 - val_one_hot_io_u: 0.5544
+Epoch 5/30
+266/266 [==============================] - ETA: 0s - loss: 0.5687 - precision: 0.8498 - recall: 0.7352 - categorical_accuracy: 0.7904 - one_hot_io_u: 0.5689
+Epoch 5: val_loss improved from 0.60250 to 0.55160, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 285s 1s/step - loss: 0.5687 - precision: 0.8498 - recall: 0.7352 - categorical_accuracy: 0.7904 - one_hot_io_u: 0.5689 - val_loss: 0.5516 - val_precision: 0.8510 - val_recall: 0.7493 - val_categorical_accuracy: 0.7973 - val_one_hot_io_u: 0.5788
+Epoch 6/30
+266/266 [==============================] - ETA: 0s - loss: 0.5453 - precision: 0.8571 - recall: 0.7454 - categorical_accuracy: 0.7985 - one_hot_io_u: 0.5813
+Epoch 6: val_loss improved from 0.55160 to 0.52872, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 284s 1s/step - loss: 0.5453 - precision: 0.8571 - recall: 0.7454 - categorical_accuracy: 0.7985 - one_hot_io_u: 0.5813 - val_loss: 0.5287 - val_precision: 0.8590 - val_recall: 0.7552 - val_categorical_accuracy: 0.8039 - val_one_hot_io_u: 0.5882
+Epoch 7/30
+266/266 [==============================] - ETA: 0s - loss: 0.5278 - precision: 0.8624 - recall: 0.7530 - categorical_accuracy: 0.8045 - one_hot_io_u: 0.5905
+Epoch 7: val_loss improved from 0.52872 to 0.50506, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 262s 988ms/step - loss: 0.5278 - precision: 0.8624 - recall: 0.7530 - categorical_accuracy: 0.8045 - one_hot_io_u: 0.5905 - val_loss: 0.5051 - val_precision: 0.8651 - val_recall: 0.7671 - val_categorical_accuracy: 0.8122 - val_one_hot_io_u: 0.5986
+Epoch 8/30
+266/266 [==============================] - ETA: 0s - loss: 0.5123 - precision: 0.8671 - recall: 0.7595 - categorical_accuracy: 0.8098 - one_hot_io_u: 0.5988
+Epoch 8: val_loss improved from 0.50506 to 0.49242, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 264s 995ms/step - loss: 0.5123 - precision: 0.8671 - recall: 0.7595 - categorical_accuracy: 0.8098 - one_hot_io_u: 0.5988 - val_loss: 0.4924 - val_precision: 0.8745 - val_recall: 0.7668 - val_categorical_accuracy: 0.8169 - val_one_hot_io_u: 0.6102
+Epoch 9/30
+266/266 [==============================] - ETA: 0s - loss: 0.5018 - precision: 0.8700 - recall: 0.7641 - categorical_accuracy: 0.8133 - one_hot_io_u: 0.6049
+Epoch 9: val_loss did not improve from 0.49242
+266/266 [==============================] - 276s 1s/step - loss: 0.5018 - precision: 0.8700 - recall: 0.7641 - categorical_accuracy: 0.8133 - one_hot_io_u: 0.6049 - val_loss: 0.5025 - val_precision: 0.8643 - val_recall: 0.7700 - val_categorical_accuracy: 0.8133 - val_one_hot_io_u: 0.6085
+Epoch 10/30
+266/266 [==============================] - ETA: 0s - loss: 0.4895 - precision: 0.8736 - recall: 0.7693 - categorical_accuracy: 0.8175 - one_hot_io_u: 0.6118
+Epoch 10: val_loss improved from 0.49242 to 0.47591, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 268s 1s/step - loss: 0.4895 - precision: 0.8736 - recall: 0.7693 - categorical_accuracy: 0.8175 - one_hot_io_u: 0.6118 - val_loss: 0.4759 - val_precision: 0.8705 - val_recall: 0.7823 - val_categorical_accuracy: 0.8220 - val_one_hot_io_u: 0.6160
+Epoch 11/30
+266/266 [==============================] - ETA: 0s - loss: 0.4791 - precision: 0.8766 - recall: 0.7737 - categorical_accuracy: 0.8210 - one_hot_io_u: 0.6176
+Epoch 11: val_loss improved from 0.47591 to 0.46856, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 263s 992ms/step - loss: 0.4791 - precision: 0.8766 - recall: 0.7737 - categorical_accuracy: 0.8210 - one_hot_io_u: 0.6176 - val_loss: 0.4686 - val_precision: 0.8763 - val_recall: 0.7808 - val_categorical_accuracy: 0.8242 - val_one_hot_io_u: 0.6214
+Epoch 12/30
+266/266 [==============================] - ETA: 0s - loss: 0.4726 - precision: 0.8783 - recall: 0.7765 - categorical_accuracy: 0.8231 - one_hot_io_u: 0.6215
+Epoch 12: val_loss did not improve from 0.46856
+266/266 [==============================] - 255s 960ms/step - loss: 0.4726 - precision: 0.8783 - recall: 0.7765 - categorical_accuracy: 0.8231 - one_hot_io_u: 0.6215 - val_loss: 0.4780 - val_precision: 0.8752 - val_recall: 0.7766 - val_categorical_accuracy: 0.8217 - val_one_hot_io_u: 0.6226
+Epoch 13/30
+266/266 [==============================] - ETA: 0s - loss: 0.4617 - precision: 0.8814 - recall: 0.7812 - categorical_accuracy: 0.8269 - one_hot_io_u: 0.6277
+Epoch 13: val_loss improved from 0.46856 to 0.45125, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 266s 1s/step - loss: 0.4617 - precision: 0.8814 - recall: 0.7812 - categorical_accuracy: 0.8269 - one_hot_io_u: 0.6277 - val_loss: 0.4513 - val_precision: 0.8810 - val_recall: 0.7881 - val_categorical_accuracy: 0.8299 - val_one_hot_io_u: 0.6327
+Epoch 14/30
+266/266 [==============================] - ETA: 0s - loss: 0.4553 - precision: 0.8830 - recall: 0.7839 - categorical_accuracy: 0.8290 - one_hot_io_u: 0.6312
+Epoch 14: val_loss improved from 0.45125 to 0.44229, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 263s 992ms/step - loss: 0.4553 - precision: 0.8830 - recall: 0.7839 - categorical_accuracy: 0.8290 - one_hot_io_u: 0.6312 - val_loss: 0.4423 - val_precision: 0.8813 - val_recall: 0.7953 - val_categorical_accuracy: 0.8335 - val_one_hot_io_u: 0.6370
+Epoch 15/30
+266/266 [==============================] - ETA: 0s - loss: 0.4488 - precision: 0.8847 - recall: 0.7867 - categorical_accuracy: 0.8312 - one_hot_io_u: 0.6348
+Epoch 15: val_loss did not improve from 0.44229
+266/266 [==============================] - 258s 973ms/step - loss: 0.4488 - precision: 0.8847 - recall: 0.7867 - categorical_accuracy: 0.8312 - one_hot_io_u: 0.6348 - val_loss: 0.4579 - val_precision: 0.8785 - val_recall: 0.7875 - val_categorical_accuracy: 0.8283 - val_one_hot_io_u: 0.6359
+Epoch 16/30
+266/266 [==============================] - ETA: 0s - loss: 0.4440 - precision: 0.8859 - recall: 0.7889 - categorical_accuracy: 0.8328 - one_hot_io_u: 0.6378
+Epoch 16: val_loss did not improve from 0.44229
+266/266 [==============================] - 265s 998ms/step - loss: 0.4440 - precision: 0.8859 - recall: 0.7889 - categorical_accuracy: 0.8328 - one_hot_io_u: 0.6378 - val_loss: 0.4840 - val_precision: 0.8707 - val_recall: 0.7798 - val_categorical_accuracy: 0.8210 - val_one_hot_io_u: 0.6273
+Epoch 17/30
+266/266 [==============================] - ETA: 0s - loss: 0.4378 - precision: 0.8875 - recall: 0.7916 - categorical_accuracy: 0.8349 - one_hot_io_u: 0.6414
+Epoch 17: val_loss did not improve from 0.44229
+266/266 [==============================] - 285s 1s/step - loss: 0.4378 - precision: 0.8875 - recall: 0.7916 - categorical_accuracy: 0.8349 - one_hot_io_u: 0.6414 - val_loss: 0.4461 - val_precision: 0.8819 - val_recall: 0.7904 - val_categorical_accuracy: 0.8314 - val_one_hot_io_u: 0.6374
+Epoch 18/30
+266/266 [==============================] - ETA: 0s - loss: 0.4336 - precision: 0.8884 - recall: 0.7935 - categorical_accuracy: 0.8363 - one_hot_io_u: 0.6438
+Epoch 18: val_loss improved from 0.44229 to 0.42199, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 278s 1s/step - loss: 0.4336 - precision: 0.8884 - recall: 0.7935 - categorical_accuracy: 0.8363 - one_hot_io_u: 0.6438 - val_loss: 0.4220 - val_precision: 0.8887 - val_recall: 0.8013 - val_categorical_accuracy: 0.8401 - val_one_hot_io_u: 0.6492
+Epoch 19/30
+266/266 [==============================] - ETA: 0s - loss: 0.4294 - precision: 0.8894 - recall: 0.7953 - categorical_accuracy: 0.8376 - one_hot_io_u: 0.6465
+Epoch 19: val_loss improved from 0.42199 to 0.41151, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 282s 1s/step - loss: 0.4294 - precision: 0.8894 - recall: 0.7953 - categorical_accuracy: 0.8376 - one_hot_io_u: 0.6465 - val_loss: 0.4115 - val_precision: 0.8920 - val_recall: 0.8044 - val_categorical_accuracy: 0.8432 - val_one_hot_io_u: 0.6546
+Epoch 20/30
+266/266 [==============================] - ETA: 0s - loss: 0.4241 - precision: 0.8907 - recall: 0.7977 - categorical_accuracy: 0.8394 - one_hot_io_u: 0.6488
+Epoch 20: val_loss did not improve from 0.41151
+266/266 [==============================] - 258s 970ms/step - loss: 0.4241 - precision: 0.8907 - recall: 0.7977 - categorical_accuracy: 0.8394 - one_hot_io_u: 0.6488 - val_loss: 0.4151 - val_precision: 0.8930 - val_recall: 0.8001 - val_categorical_accuracy: 0.8419 - val_one_hot_io_u: 0.6524
+Epoch 21/30
+266/266 [==============================] - ETA: 0s - loss: 0.4196 - precision: 0.8919 - recall: 0.7998 - categorical_accuracy: 0.8410 - one_hot_io_u: 0.6520
+Epoch 21: val_loss did not improve from 0.41151
+266/266 [==============================] - 259s 977ms/step - loss: 0.4196 - precision: 0.8919 - recall: 0.7998 - categorical_accuracy: 0.8410 - one_hot_io_u: 0.6520 - val_loss: 0.4322 - val_precision: 0.8861 - val_recall: 0.7996 - val_categorical_accuracy: 0.8377 - val_one_hot_io_u: 0.6480
+Epoch 22/30
+266/266 [==============================] - ETA: 0s - loss: 0.4171 - precision: 0.8923 - recall: 0.8010 - categorical_accuracy: 0.8418 - one_hot_io_u: 0.6533
+Epoch 22: val_loss did not improve from 0.41151
+266/266 [==============================] - 260s 982ms/step - loss: 0.4171 - precision: 0.8923 - recall: 0.8010 - categorical_accuracy: 0.8418 - one_hot_io_u: 0.6533 - val_loss: 0.4217 - val_precision: 0.8869 - val_recall: 0.8027 - val_categorical_accuracy: 0.8398 - val_one_hot_io_u: 0.6500
+Epoch 23/30
+266/266 [==============================] - ETA: 0s - loss: 0.4134 - precision: 0.8931 - recall: 0.8027 - categorical_accuracy: 0.8430 - one_hot_io_u: 0.6551
+Epoch 23: val_loss improved from 0.41151 to 0.40218, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 272s 1s/step - loss: 0.4134 - precision: 0.8931 - recall: 0.8027 - categorical_accuracy: 0.8430 - one_hot_io_u: 0.6551 - val_loss: 0.4022 - val_precision: 0.8928 - val_recall: 0.8101 - val_categorical_accuracy: 0.8464 - val_one_hot_io_u: 0.6619
+Epoch 24/30
+266/266 [==============================] - ETA: 0s - loss: 0.4117 - precision: 0.8936 - recall: 0.8035 - categorical_accuracy: 0.8436 - one_hot_io_u: 0.6563
+Epoch 24: val_loss did not improve from 0.40218
+266/266 [==============================] - 260s 981ms/step - loss: 0.4117 - precision: 0.8936 - recall: 0.8035 - categorical_accuracy: 0.8436 - one_hot_io_u: 0.6563 - val_loss: 0.4047 - val_precision: 0.8892 - val_recall: 0.8128 - val_categorical_accuracy: 0.8460 - val_one_hot_io_u: 0.6579
+Epoch 25/30
+266/266 [==============================] - ETA: 0s - loss: 0.4064 - precision: 0.8948 - recall: 0.8058 - categorical_accuracy: 0.8454 - one_hot_io_u: 0.6594
+Epoch 25: val_loss improved from 0.40218 to 0.39190, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 298s 1s/step - loss: 0.4064 - precision: 0.8948 - recall: 0.8058 - categorical_accuracy: 0.8454 - one_hot_io_u: 0.6594 - val_loss: 0.3919 - val_precision: 0.8960 - val_recall: 0.8134 - val_categorical_accuracy: 0.8497 - val_one_hot_io_u: 0.6662
+Epoch 26/30
+266/266 [==============================] - ETA: 0s - loss: 0.4038 - precision: 0.8954 - recall: 0.8070 - categorical_accuracy: 0.8462 - one_hot_io_u: 0.6605
+Epoch 26: val_loss improved from 0.39190 to 0.38542, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 280s 1s/step - loss: 0.4038 - precision: 0.8954 - recall: 0.8070 - categorical_accuracy: 0.8462 - one_hot_io_u: 0.6605 - val_loss: 0.3854 - val_precision: 0.8982 - val_recall: 0.8159 - val_categorical_accuracy: 0.8520 - val_one_hot_io_u: 0.6693
+Epoch 27/30
+266/266 [==============================] - ETA: 0s - loss: 0.4007 - precision: 0.8961 - recall: 0.8085 - categorical_accuracy: 0.8473 - one_hot_io_u: 0.6627
+Epoch 27: val_loss improved from 0.38542 to 0.38379, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 283s 1s/step - loss: 0.4007 - precision: 0.8961 - recall: 0.8085 - categorical_accuracy: 0.8473 - one_hot_io_u: 0.6627 - val_loss: 0.3838 - val_precision: 0.8978 - val_recall: 0.8175 - val_categorical_accuracy: 0.8527 - val_one_hot_io_u: 0.6711
+Epoch 28/30
+266/266 [==============================] - ETA: 0s - loss: 0.3972 - precision: 0.8969 - recall: 0.8101 - categorical_accuracy: 0.8485 - one_hot_io_u: 0.6647
+Epoch 28: val_loss improved from 0.38379 to 0.37968, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 264s 996ms/step - loss: 0.3972 - precision: 0.8969 - recall: 0.8101 - categorical_accuracy: 0.8485 - one_hot_io_u: 0.6647 - val_loss: 0.3797 - val_precision: 0.8977 - val_recall: 0.8211 - val_categorical_accuracy: 0.8543 - val_one_hot_io_u: 0.6748
+Epoch 29/30
+266/266 [==============================] - ETA: 0s - loss: 0.3962 - precision: 0.8970 - recall: 0.8105 - categorical_accuracy: 0.8488 - one_hot_io_u: 0.6653
+Epoch 29: val_loss improved from 0.37968 to 0.37328, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 263s 989ms/step - loss: 0.3962 - precision: 0.8970 - recall: 0.8105 - categorical_accuracy: 0.8488 - one_hot_io_u: 0.6653 - val_loss: 0.3733 - val_precision: 0.8989 - val_recall: 0.8249 - val_categorical_accuracy: 0.8568 - val_one_hot_io_u: 0.6785
+Epoch 30/30
+266/266 [==============================] - ETA: 0s - loss: 0.3934 - precision: 0.8977 - recall: 0.8119 - categorical_accuracy: 0.8498 - one_hot_io_u: 0.6666
+Epoch 30: val_loss improved from 0.37328 to 0.37270, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
+266/266 [==============================] - 265s 998ms/step - loss: 0.3934 - precision: 0.8977 - recall: 0.8119 - categorical_accuracy: 0.8498 - one_hot_io_u: 0.6666 - val_loss: 0.3727 - val_precision: 0.9007 - val_recall: 0.8224 - val_categorical_accuracy: 0.8565 - val_one_hot_io_u: 0.6781
+****************************************************************************
+****************************** evaluating model... *************************
+************************************************
+************************************************
+Validation
+1222/1222 [==============================] - 36s 22ms/step - loss: 0.3723 - precision: 0.9009 - recall: 0.8225 - categorical_accuracy: 0.8566 - one_hot_io_u: 0.6781
+loss: 0.3722515106201172
+precision: 0.9008649587631226
+recall: 0.8225432634353638
+categorical_accuracy: 0.8566350340843201
+one_hot_io_u: 0.6780571341514587
+
+
+****************************************************************************
+****************************** saving parameters... ************************
+****************************************************************************
+*************** saving model config and history object... ******************
+****************************************************************************
+****************************** saving plots... *****************************
+Saving plots and model visualization at /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1...
+****************************************************************************
+****************************** saving models... ****************************
+****************************************************************************
+
+
+
+
+
+
Save the config file
+
+
+
+from pathlib import Path
+import shutil
+
+config_file = Path(config_file)
+drive_config_file = Path(unet_config.MODEL_DIR / f"{str(config_file).split('/')[-1]}")
+
+# Create the target directory if it doesn't exist
+drive_config_file.parent.mkdir(parents=True, exist_ok=True)
+
+# Copy the file
+shutil.copy(config_file, drive_config_file)
+
+print(f"File copied from {config_file} to {drive_config_file}")
+
+File copied from servir-aces/config.env to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/config.envLoad the logs files via TensorBoard
+Tensorboard provides a unique way to view and interact with the logs while the model is being trained. Learn more here. Here we only show you how you can load them to tensorboard with our training logs.
+
+
+
+# Load the TensorBoard notebook extension
+%load_ext tensorboard
+
+
+log_dir_unet = f"{str(unet_config.MODEL_DIR)}/logs"
+log_dir_unet
+
+'/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/logs'
+
+
+%tensorboard --logdir "{log_dir_unet}"
+
+Reusing TensorBoard on port 6007 (pid 5630), started 0:02:00 ago. (Use '!kill 5630' to kill it.)
+
+
+Load the Saved U-Net Model
+Load the saved model
+
+
+
+import tensorflow as tf
+
+
+unet_model = tf.keras.models.load_model(f"{str(unet_config.MODEL_DIR)}/trained-model")
+
+
+print(unet_model.summary())
+
+Model: "unet"
+__________________________________________________________________________________________________
+ Layer (type) Output Shape Param # Connected to
+==================================================================================================
+ input_1 (InputLayer) [(None, None, None, 8)] 0 []
+
+ conv2d (Conv2D) (None, None, None, 32) 2336 ['input_1[0][0]']
+
+ batch_normalization (Batch (None, None, None, 32) 128 ['conv2d[0][0]']
+ Normalization)
+
+ activation (Activation) (None, None, None, 32) 0 ['batch_normalization[0][0]']
+
+ activation_1 (Activation) (None, None, None, 32) 0 ['activation[0][0]']
+
+ separable_conv2d (Separabl (None, None, None, 64) 2400 ['activation_1[0][0]']
+ eConv2D)
+
+ batch_normalization_1 (Bat (None, None, None, 64) 256 ['separable_conv2d[0][0]']
+ chNormalization)
+
+ activation_2 (Activation) (None, None, None, 64) 0 ['batch_normalization_1[0][0]'
+ ]
+
+ separable_conv2d_1 (Separa (None, None, None, 64) 4736 ['activation_2[0][0]']
+ bleConv2D)
+
+ batch_normalization_2 (Bat (None, None, None, 64) 256 ['separable_conv2d_1[0][0]']
+ chNormalization)
+
+ max_pooling2d (MaxPooling2 (None, None, None, 64) 0 ['batch_normalization_2[0][0]'
+ D) ]
+
+ conv2d_1 (Conv2D) (None, None, None, 64) 2112 ['activation[0][0]']
+
+ add (Add) (None, None, None, 64) 0 ['max_pooling2d[0][0]',
+ 'conv2d_1[0][0]']
+
+ activation_3 (Activation) (None, None, None, 64) 0 ['add[0][0]']
+
+ separable_conv2d_2 (Separa (None, None, None, 128) 8896 ['activation_3[0][0]']
+ bleConv2D)
+
+ batch_normalization_3 (Bat (None, None, None, 128) 512 ['separable_conv2d_2[0][0]']
+ chNormalization)
+
+ activation_4 (Activation) (None, None, None, 128) 0 ['batch_normalization_3[0][0]'
+ ]
+
+ separable_conv2d_3 (Separa (None, None, None, 128) 17664 ['activation_4[0][0]']
+ bleConv2D)
+
+ batch_normalization_4 (Bat (None, None, None, 128) 512 ['separable_conv2d_3[0][0]']
+ chNormalization)
+
+ max_pooling2d_1 (MaxPoolin (None, None, None, 128) 0 ['batch_normalization_4[0][0]'
+ g2D) ]
+
+ conv2d_2 (Conv2D) (None, None, None, 128) 8320 ['add[0][0]']
+
+ add_1 (Add) (None, None, None, 128) 0 ['max_pooling2d_1[0][0]',
+ 'conv2d_2[0][0]']
+
+ activation_5 (Activation) (None, None, None, 128) 0 ['add_1[0][0]']
+
+ separable_conv2d_4 (Separa (None, None, None, 256) 34176 ['activation_5[0][0]']
+ bleConv2D)
+
+ batch_normalization_5 (Bat (None, None, None, 256) 1024 ['separable_conv2d_4[0][0]']
+ chNormalization)
+
+ activation_6 (Activation) (None, None, None, 256) 0 ['batch_normalization_5[0][0]'
+ ]
+
+ separable_conv2d_5 (Separa (None, None, None, 256) 68096 ['activation_6[0][0]']
+ bleConv2D)
+
+ batch_normalization_6 (Bat (None, None, None, 256) 1024 ['separable_conv2d_5[0][0]']
+ chNormalization)
+
+ max_pooling2d_2 (MaxPoolin (None, None, None, 256) 0 ['batch_normalization_6[0][0]'
+ g2D) ]
+
+ conv2d_3 (Conv2D) (None, None, None, 256) 33024 ['add_1[0][0]']
+
+ add_2 (Add) (None, None, None, 256) 0 ['max_pooling2d_2[0][0]',
+ 'conv2d_3[0][0]']
+
+ activation_7 (Activation) (None, None, None, 256) 0 ['add_2[0][0]']
+
+ conv2d_transpose (Conv2DTr (None, None, None, 256) 590080 ['activation_7[0][0]']
+ anspose)
+
+ batch_normalization_7 (Bat (None, None, None, 256) 1024 ['conv2d_transpose[0][0]']
+ chNormalization)
+
+ activation_8 (Activation) (None, None, None, 256) 0 ['batch_normalization_7[0][0]'
+ ]
+
+ conv2d_transpose_1 (Conv2D (None, None, None, 256) 590080 ['activation_8[0][0]']
+ Transpose)
+
+ batch_normalization_8 (Bat (None, None, None, 256) 1024 ['conv2d_transpose_1[0][0]']
+ chNormalization)
+
+ up_sampling2d_1 (UpSamplin (None, None, None, 256) 0 ['add_2[0][0]']
+ g2D)
+
+ up_sampling2d (UpSampling2 (None, None, None, 256) 0 ['batch_normalization_8[0][0]'
+ D) ]
+
+ conv2d_4 (Conv2D) (None, None, None, 256) 65792 ['up_sampling2d_1[0][0]']
+
+ add_3 (Add) (None, None, None, 256) 0 ['up_sampling2d[0][0]',
+ 'conv2d_4[0][0]']
+
+ activation_9 (Activation) (None, None, None, 256) 0 ['add_3[0][0]']
+
+ conv2d_transpose_2 (Conv2D (None, None, None, 128) 295040 ['activation_9[0][0]']
+ Transpose)
+
+ batch_normalization_9 (Bat (None, None, None, 128) 512 ['conv2d_transpose_2[0][0]']
+ chNormalization)
+
+ activation_10 (Activation) (None, None, None, 128) 0 ['batch_normalization_9[0][0]'
+ ]
+
+ conv2d_transpose_3 (Conv2D (None, None, None, 128) 147584 ['activation_10[0][0]']
+ Transpose)
+
+ batch_normalization_10 (Ba (None, None, None, 128) 512 ['conv2d_transpose_3[0][0]']
+ tchNormalization)
+
+ up_sampling2d_3 (UpSamplin (None, None, None, 256) 0 ['add_3[0][0]']
+ g2D)
+
+ up_sampling2d_2 (UpSamplin (None, None, None, 128) 0 ['batch_normalization_10[0][0]
+ g2D) ']
+
+ conv2d_5 (Conv2D) (None, None, None, 128) 32896 ['up_sampling2d_3[0][0]']
+
+ add_4 (Add) (None, None, None, 128) 0 ['up_sampling2d_2[0][0]',
+ 'conv2d_5[0][0]']
+
+ activation_11 (Activation) (None, None, None, 128) 0 ['add_4[0][0]']
+
+ conv2d_transpose_4 (Conv2D (None, None, None, 64) 73792 ['activation_11[0][0]']
+ Transpose)
+
+ batch_normalization_11 (Ba (None, None, None, 64) 256 ['conv2d_transpose_4[0][0]']
+ tchNormalization)
+
+ activation_12 (Activation) (None, None, None, 64) 0 ['batch_normalization_11[0][0]
+ ']
+
+ conv2d_transpose_5 (Conv2D (None, None, None, 64) 36928 ['activation_12[0][0]']
+ Transpose)
+
+ batch_normalization_12 (Ba (None, None, None, 64) 256 ['conv2d_transpose_5[0][0]']
+ tchNormalization)
+
+ up_sampling2d_5 (UpSamplin (None, None, None, 128) 0 ['add_4[0][0]']
+ g2D)
+
+ up_sampling2d_4 (UpSamplin (None, None, None, 64) 0 ['batch_normalization_12[0][0]
+ g2D) ']
+
+ conv2d_6 (Conv2D) (None, None, None, 64) 8256 ['up_sampling2d_5[0][0]']
+
+ add_5 (Add) (None, None, None, 64) 0 ['up_sampling2d_4[0][0]',
+ 'conv2d_6[0][0]']
+
+ activation_13 (Activation) (None, None, None, 64) 0 ['add_5[0][0]']
+
+ conv2d_transpose_6 (Conv2D (None, None, None, 32) 18464 ['activation_13[0][0]']
+ Transpose)
+
+ batch_normalization_13 (Ba (None, None, None, 32) 128 ['conv2d_transpose_6[0][0]']
+ tchNormalization)
+
+ activation_14 (Activation) (None, None, None, 32) 0 ['batch_normalization_13[0][0]
+ ']
+
+ conv2d_transpose_7 (Conv2D (None, None, None, 32) 9248 ['activation_14[0][0]']
+ Transpose)
+
+ batch_normalization_14 (Ba (None, None, None, 32) 128 ['conv2d_transpose_7[0][0]']
+ tchNormalization)
+
+ up_sampling2d_7 (UpSamplin (None, None, None, 64) 0 ['add_5[0][0]']
+ g2D)
+
+ up_sampling2d_6 (UpSamplin (None, None, None, 32) 0 ['batch_normalization_14[0][0]
+ g2D) ']
+
+ conv2d_7 (Conv2D) (None, None, None, 32) 2080 ['up_sampling2d_7[0][0]']
+
+ add_6 (Add) (None, None, None, 32) 0 ['up_sampling2d_6[0][0]',
+ 'conv2d_7[0][0]']
+
+ final_conv (Conv2D) (None, None, None, 5) 1445 ['add_6[0][0]']
+
+==================================================================================================
+Total params: 2060997 (7.86 MB)
+Trainable params: 2057221 (7.85 MB)
+Non-trainable params: 3776 (14.75 KB)
+__________________________________________________________________________________________________
+NoneInference using Saved U-Net Model
+Now we can use the saved model to start the export of the prediction of the image. For prediction, you would need to first prepare your image data. We have already exported the image needed here, which we will use for now. See this notebook to understand how we did it.
+In addition, this notebook shows how you can then use the image to predict from the saved Model.
+In any case, you now have the prediction in the Earth Engine as image.
+DNN Model
+Setup any changes in the config file for DNN Model
+There are few config variables that needs to be changed for running a DNN model. First would be the data itself so let’s change the DATADIR. We also need to change our output directory using MODEL_DIR_NAME. This is the sub-directory inside the OUTPUT_DIR for this model run. We also need to specify this is the DNN model that we want to run. We have MODEL_TYPE parameter for that. Currently, it supports unet, dnn, and cnn (case sensitive) models; default being unet. Make other changes, as appropriate.
DATADIR = "datasets/dnn_planet_wo_indices"
+MODEL_DIR_NAME = "dnn_v1"
+MODEL_TYPE = "dnn"Update the config file programtically
+
+
+
+DATADIR = "datasets/dnn_planet_wo_indices" # @param {type:"string"}
+# PATCH_SHAPE, USE_ELEVATION, USE_S1, TRAIN_SIZE, TEST_SIZE, VAL_SIZE
+# BATCH_SIZE, EPOCHS are converted to their appropriate type.
+MODEL_DIR_NAME = "dnn_v1" # @param {type:"string"}
+MODEL_TYPE = "dnn" # @param {type:"string"}
+BATCH_SIZE = "32" # @param {type:"string"}
+EPOCHS = "30" # @param {type:"string"}
+
+
+dnn_config_settings = {
+ "DATADIR": DATADIR,
+ "MODEL_DIR_NAME": MODEL_DIR_NAME,
+ "MODEL_TYPE": MODEL_TYPE,
+ "BATCH_SIZE": BATCH_SIZE,
+ "EPOCHS": EPOCHS,
+}
+
+
+for config_key in dnn_config_settings:
+ dotenv.set_key(dotenv_path=config_file,
+ key_to_set=config_key,
+ value_to_set=dnn_config_settings[config_key]
+ )Load config file variables for DNN Model
+
+
+
+dnn_config = Config(config_file=config_file, override=True)
+
+BASEDIR: /content
+DATADIR: /content/datasets/dnn_planet_wo_indices
+using features: ['red_before', 'green_before', 'blue_before', 'nir_before', 'red_during', 'green_during', 'blue_during', 'nir_during']
+using labels: ['class']Most of the config in the config.env is now available via the config instance. Let’s check few of them here.
+
+
+dnn_config.TRAINING_DIR, dnn_config.OUTPUT_DIR, dnn_config.BATCH_SIZE, dnn_config.MODEL_TYPE
+
+(PosixPath('/content/datasets/dnn_planet_wo_indices/training'),
+ PosixPath('/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output'),
+ 32,
+ 'dnn')Load ModelTrainer class
+Next, let’s make an instance of the ModelTrainer object. The ModelTrainer class provides various tools for training, buidling, compiling, and running specified deep learning models.
+
+
+dnn_model_trainer = ModelTrainer(dnn_config, seed=42)
+
+Using seed: 42Train and Save DNN model
+
+
+
+dnn_model_trainer.train_model()
+
+****************************************************************************
+****************************** Clear Session... ****************************
+****************************************************************************
+****************************** Configure memory growth... ************************
+ > Found 1 GPUs
+****************************************************************************
+****************************** creating datasets... ************************
+Loading dataset from /content/datasets/dnn_planet_wo_indices/training/*
+Loading dataset from /content/datasets/dnn_planet_wo_indices/validation/*
+Loading dataset from /content/datasets/dnn_planet_wo_indices/testing/*
+Printing dataset info:
+Training
+inputs: float32 (32, 1, 8)
+tf.Tensor(
+[[[0.06445 0.0383 0.09815 0.06755 0.269975 0.207325 0.11135
+ 0.060025]]
+
+ [[0.075925 0.02705 0.08695 0.054775 0.235575 0.291625 0.1049
+ 0.0364 ]]
+
+ [[0.043625 0.025 0.064175 0.04265 0.22 0.225 0.062025
+ 0.03195 ]]
+
+ [[0.07915 0.05365 0.1054 0.093425 0.257325 0.28345 0.1119
+ 0.079675]]
+
+ [[0.06945 0.025825 0.10755 0.062125 0.245125 0.28365 0.116975
+ 0.0485 ]]
+
+ [[0.092425 0.07485 0.10285 0.09645 0.238575 0.252075 0.123025
+ 0.094825]]
+
+ [[0.0555 0.02955 0.087325 0.09095 0.230625 0.298175 0.08075
+ 0.0524 ]]
+
+ [[0.0643 0.021275 0.085875 0.0431 0.248075 0.27395 0.098625
+ 0.0284 ]]
+
+ [[0.0747 0.047675 0.094625 0.072125 0.253125 0.267225 0.09725
+ 0.063825]]
+
+ [[0.0626 0.023575 0.083675 0.04705 0.20645 0.2539 0.10465
+ 0.0317 ]]
+
+ [[0.072725 0.030975 0.10185 0.0666 0.26585 0.38635 0.11875
+ 0.045725]]
+
+ [[0.0713 0.0289 0.09355 0.06415 0.236675 0.2564 0.10215
+ 0.049475]]
+
+ [[0.086175 0.077875 0.10745 0.0709 0.263475 0.289175 0.1188
+ 0.075975]]
+
+ [[0.079575 0.027525 0.102325 0.054775 0.253875 0.2761 0.111325
+ 0.03985 ]]
+
+ [[0.08825 0.0803 0.1016 0.0862 0.265025 0.260925 0.115625
+ 0.092325]]
+
+ [[0.08025 0.10325 0.1034 0.13695 0.2283 0.246475 0.109975
+ 0.124 ]]
+
+ [[0.077775 0.029875 0.0953 0.0546 0.235325 0.266 0.122125
+ 0.0469 ]]
+
+ [[0.0778 0.024025 0.103525 0.053975 0.23395 0.263625 0.116025
+ 0.036825]]
+
+ [[0.089475 0.070675 0.10515 0.09125 0.257725 0.254375 0.132675
+ 0.09745 ]]
+
+ [[0.0785 0.026275 0.105575 0.051025 0.2552 0.2522 0.120125
+ 0.034825]]
+
+ [[0.07945 0.045775 0.094475 0.0652 0.264175 0.335825 0.1147
+ 0.059275]]
+
+ [[0.04725 0.031125 0.08165 0.065325 0.23025 0.2299 0.0981
+ 0.060525]]
+
+ [[0.07475 0.02425 0.10205 0.04945 0.263225 0.178625 0.112775
+ 0.0373 ]]
+
+ [[0.079875 0.0259 0.105875 0.05245 0.2505 0.269425 0.12245
+ 0.034975]]
+
+ [[0.0746 0.033975 0.104075 0.0598 0.25375 0.29345 0.117325
+ 0.04885 ]]
+
+ [[0.067125 0.026625 0.093575 0.05095 0.255925 0.231575 0.10545
+ 0.0415 ]]
+
+ [[0.076325 0.02685 0.10615 0.05585 0.25805 0.276325 0.116725
+ 0.0417 ]]
+
+ [[0.061275 0.023975 0.086025 0.041375 0.199425 0.255125 0.111
+ 0.030825]]
+
+ [[0.059725 0.0203 0.0877 0.0434 0.230125 0.251975 0.105675
+ 0.027225]]
+
+ [[0.07205 0.02285 0.094025 0.04865 0.2084 0.247225 0.119175
+ 0.03225 ]]
+
+ [[0.0405 0.023025 0.06625 0.0505 0.218225 0.258275 0.0802
+ 0.03875 ]]
+
+ [[0.07005 0.023125 0.111925 0.0475 0.2716 0.2495 0.124725
+ 0.0313 ]]], shape=(32, 1, 8), dtype=float32)
+outputs: float32 (32, 1, 5)
+tf.Tensor(
+[[[0. 1. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 0. 0. 1. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 0. 0. 1. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[1. 0. 0. 0. 0.]]
+
+ [[0. 0. 1. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 0. 0. 1. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 0. 0. 1. 0.]]
+
+ [[0. 0. 0. 1. 0.]]
+
+ [[0. 0. 0. 1. 0.]]
+
+ [[0. 0. 0. 1. 0.]]
+
+ [[0. 0. 0. 1. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 0. 1. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]
+
+ [[0. 0. 0. 0. 1.]]
+
+ [[0. 0. 1. 0. 0.]]
+
+ [[0. 1. 0. 0. 0.]]], shape=(32, 1, 5), dtype=float32)
+Testing
+inputs: float32 (1, 1, 8)
+tf.Tensor(
+[[[0.06205 0.0342 0.081075 0.0639 0.24245 0.251675 0.086575
+ 0.054175]]], shape=(1, 1, 8), dtype=float32)
+outputs: float32 (1, 1, 5)
+tf.Tensor([[[1. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)
+Validation
+inputs: float32 (1, 1, 8)
+tf.Tensor(
+[[[0.067225 0.031725 0.092275 0.07245 0.23165 0.2267 0.103025
+ 0.05155 ]]], shape=(1, 1, 8), dtype=float32)
+outputs: float32 (1, 1, 5)
+tf.Tensor([[[0. 0. 0. 1. 0.]]], shape=(1, 1, 5), dtype=float32)
+****************************************************************************
+************************ building and compiling model... *******************
+INITIAL_BIAS: None
+Model: "model"
+_________________________________________________________________
+ Layer (type) Output Shape Param #
+=================================================================
+ input_layer (InputLayer) [(None, None, 8)] 0
+
+ dense (Dense) (None, None, 256) 2304
+
+ dropout (Dropout) (None, None, 256) 0
+
+ dense_1 (Dense) (None, None, 128) 32896
+
+ dropout_1 (Dropout) (None, None, 128) 0
+
+ dense_2 (Dense) (None, None, 64) 8256
+
+ dropout_2 (Dropout) (None, None, 64) 0
+
+ dense_3 (Dense) (None, None, 32) 2080
+
+ dropout_3 (Dropout) (None, None, 32) 0
+
+ dense_4 (Dense) (None, None, 5) 165
+
+=================================================================
+Total params: 45701 (178.52 KB)
+Trainable params: 45701 (178.52 KB)
+Non-trainable params: 0 (0.00 Byte)
+_________________________________________________________________
+None
+****************************************************************************
+************************ preparing output directory... *********************
+> Saving models and results at /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1...
+****************************************************************************
+****************************** training model... ***************************
+Epoch 1/30
+264/266 [============================>.] - ETA: 0s - loss: 1.2595 - precision: 0.6700 - recall: 0.2397 - categorical_accuracy: 0.4964 - one_hot_io_u: 0.2151
+Epoch 1: val_loss improved from inf to 1.15955, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
+266/266 [==============================] - 31s 99ms/step - loss: 1.2623 - precision: 0.6685 - recall: 0.2401 - categorical_accuracy: 0.4960 - one_hot_io_u: 0.2148 - val_loss: 1.1595 - val_precision: 0.6827 - val_recall: 0.4064 - val_categorical_accuracy: 0.5807 - val_one_hot_io_u: 0.2866
+Epoch 2/30
+265/266 [============================>.] - ETA: 0s - loss: 0.9196 - precision: 0.7593 - recall: 0.5601 - categorical_accuracy: 0.6874 - one_hot_io_u: 0.4127
+Epoch 2: val_loss improved from 1.15955 to 0.87784, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
+266/266 [==============================] - 16s 60ms/step - loss: 0.9206 - precision: 0.7592 - recall: 0.5596 - categorical_accuracy: 0.6871 - one_hot_io_u: 0.4125 - val_loss: 0.8778 - val_precision: 0.7574 - val_recall: 0.6090 - val_categorical_accuracy: 0.6968 - val_one_hot_io_u: 0.4428
+Epoch 3/30
+266/266 [==============================] - ETA: 0s - loss: 0.8111 - precision: 0.7857 - recall: 0.6387 - categorical_accuracy: 0.7218 - one_hot_io_u: 0.4734
+Epoch 3: val_loss did not improve from 0.87784
+266/266 [==============================] - 23s 89ms/step - loss: 0.8111 - precision: 0.7857 - recall: 0.6387 - categorical_accuracy: 0.7218 - one_hot_io_u: 0.4734 - val_loss: 0.8998 - val_precision: 0.8096 - val_recall: 0.4193 - val_categorical_accuracy: 0.7288 - val_one_hot_io_u: 0.4815
+Epoch 4/30
+261/266 [============================>.] - ETA: 0s - loss: 0.7535 - precision: 0.8003 - recall: 0.6697 - categorical_accuracy: 0.7462 - one_hot_io_u: 0.5193
+Epoch 4: val_loss improved from 0.87784 to 0.82494, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
+266/266 [==============================] - 16s 61ms/step - loss: 0.7584 - precision: 0.7999 - recall: 0.6640 - categorical_accuracy: 0.7440 - one_hot_io_u: 0.5169 - val_loss: 0.8249 - val_precision: 0.8067 - val_recall: 0.5383 - val_categorical_accuracy: 0.7317 - val_one_hot_io_u: 0.4902
+Epoch 5/30
+265/266 [============================>.] - ETA: 0s - loss: 0.7328 - precision: 0.8096 - recall: 0.6695 - categorical_accuracy: 0.7466 - one_hot_io_u: 0.5352
+Epoch 5: val_loss improved from 0.82494 to 0.74722, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
+266/266 [==============================] - 18s 66ms/step - loss: 0.7328 - precision: 0.8096 - recall: 0.6694 - categorical_accuracy: 0.7467 - one_hot_io_u: 0.5352 - val_loss: 0.7472 - val_precision: 0.8216 - val_recall: 0.6514 - val_categorical_accuracy: 0.7579 - val_one_hot_io_u: 0.5588
+Epoch 6/30
+264/266 [============================>.] - ETA: 0s - loss: 0.7254 - precision: 0.8092 - recall: 0.6757 - categorical_accuracy: 0.7493 - one_hot_io_u: 0.5396
+Epoch 6: val_loss improved from 0.74722 to 0.69727, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
+266/266 [==============================] - 16s 61ms/step - loss: 0.7268 - precision: 0.8086 - recall: 0.6749 - categorical_accuracy: 0.7487 - one_hot_io_u: 0.5391 - val_loss: 0.6973 - val_precision: 0.8324 - val_recall: 0.6755 - val_categorical_accuracy: 0.7696 - val_one_hot_io_u: 0.5790
+Epoch 7/30
+265/266 [============================>.] - ETA: 0s - loss: 0.7175 - precision: 0.8121 - recall: 0.6877 - categorical_accuracy: 0.7531 - one_hot_io_u: 0.5439
+Epoch 7: val_loss improved from 0.69727 to 0.69563, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
+266/266 [==============================] - 16s 61ms/step - loss: 0.7175 - precision: 0.8121 - recall: 0.6878 - categorical_accuracy: 0.7532 - one_hot_io_u: 0.5440 - val_loss: 0.6956 - val_precision: 0.8141 - val_recall: 0.6905 - val_categorical_accuracy: 0.7562 - val_one_hot_io_u: 0.5521
+Epoch 8/30
+265/266 [============================>.] - ETA: 0s - loss: 0.7065 - precision: 0.8111 - recall: 0.6887 - categorical_accuracy: 0.7545 - one_hot_io_u: 0.5516
+Epoch 8: val_loss improved from 0.69563 to 0.67199, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
+266/266 [==============================] - 24s 92ms/step - loss: 0.7066 - precision: 0.8109 - recall: 0.6885 - categorical_accuracy: 0.7542 - one_hot_io_u: 0.5513 - val_loss: 0.6720 - val_precision: 0.8114 - val_recall: 0.7196 - val_categorical_accuracy: 0.7716 - val_one_hot_io_u: 0.5806
+Epoch 9/30
+266/266 [==============================] - ETA: 0s - loss: 0.6927 - precision: 0.8161 - recall: 0.6925 - categorical_accuracy: 0.7579 - one_hot_io_u: 0.5574
+Epoch 9: val_loss improved from 0.67199 to 0.66129, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
+266/266 [==============================] - 24s 92ms/step - loss: 0.6927 - precision: 0.8161 - recall: 0.6925 - categorical_accuracy: 0.7579 - one_hot_io_u: 0.5574 - val_loss: 0.6613 - val_precision: 0.8124 - val_recall: 0.7205 - val_categorical_accuracy: 0.7704 - val_one_hot_io_u: 0.5848
+Epoch 10/30
+264/266 [============================>.] - ETA: 0s - loss: 0.6922 - precision: 0.8143 - recall: 0.6990 - categorical_accuracy: 0.7613 - one_hot_io_u: 0.5631
+Epoch 10: val_loss did not improve from 0.66129
+266/266 [==============================] - 23s 87ms/step - loss: 0.6936 - precision: 0.8139 - recall: 0.6985 - categorical_accuracy: 0.7607 - one_hot_io_u: 0.5626 - val_loss: 0.6650 - val_precision: 0.8180 - val_recall: 0.7234 - val_categorical_accuracy: 0.7720 - val_one_hot_io_u: 0.5882
+Epoch 11/30
+263/266 [============================>.] - ETA: 0s - loss: 0.6978 - precision: 0.8120 - recall: 0.6963 - categorical_accuracy: 0.7553 - one_hot_io_u: 0.5598
+Epoch 11: val_loss did not improve from 0.66129
+266/266 [==============================] - 23s 87ms/step - loss: 0.6984 - precision: 0.8117 - recall: 0.6962 - categorical_accuracy: 0.7553 - one_hot_io_u: 0.5595 - val_loss: 0.6796 - val_precision: 0.8190 - val_recall: 0.7076 - val_categorical_accuracy: 0.7633 - val_one_hot_io_u: 0.5847
+Epoch 12/30
+263/266 [============================>.] - ETA: 0s - loss: 0.6804 - precision: 0.8173 - recall: 0.7035 - categorical_accuracy: 0.7622 - one_hot_io_u: 0.5677
+Epoch 12: val_loss improved from 0.66129 to 0.65464, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
+266/266 [==============================] - 17s 63ms/step - loss: 0.6806 - precision: 0.8177 - recall: 0.7042 - categorical_accuracy: 0.7626 - one_hot_io_u: 0.5679 - val_loss: 0.6546 - val_precision: 0.8114 - val_recall: 0.7230 - val_categorical_accuracy: 0.7745 - val_one_hot_io_u: 0.5955
+Epoch 13/30
+261/266 [============================>.] - ETA: 0s - loss: 0.6858 - precision: 0.8154 - recall: 0.6991 - categorical_accuracy: 0.7605 - one_hot_io_u: 0.5657
+Epoch 13: val_loss did not improve from 0.65464
+266/266 [==============================] - 15s 56ms/step - loss: 0.6863 - precision: 0.8152 - recall: 0.6998 - categorical_accuracy: 0.7609 - one_hot_io_u: 0.5665 - val_loss: 0.6637 - val_precision: 0.8033 - val_recall: 0.7338 - val_categorical_accuracy: 0.7712 - val_one_hot_io_u: 0.5907
+Epoch 14/30
+264/266 [============================>.] - ETA: 0s - loss: 0.6880 - precision: 0.8123 - recall: 0.6987 - categorical_accuracy: 0.7601 - one_hot_io_u: 0.5650
+Epoch 14: val_loss did not improve from 0.65464
+266/266 [==============================] - 24s 90ms/step - loss: 0.6894 - precision: 0.8119 - recall: 0.6984 - categorical_accuracy: 0.7595 - one_hot_io_u: 0.5648 - val_loss: 0.7179 - val_precision: 0.7723 - val_recall: 0.6801 - val_categorical_accuracy: 0.7309 - val_one_hot_io_u: 0.5505
+Epoch 15/30
+263/266 [============================>.] - ETA: 0s - loss: 0.6748 - precision: 0.8167 - recall: 0.7041 - categorical_accuracy: 0.7643 - one_hot_io_u: 0.5692
+Epoch 15: val_loss improved from 0.65464 to 0.64675, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
+266/266 [==============================] - 16s 60ms/step - loss: 0.6758 - precision: 0.8164 - recall: 0.7037 - categorical_accuracy: 0.7639 - one_hot_io_u: 0.5688 - val_loss: 0.6467 - val_precision: 0.8123 - val_recall: 0.7363 - val_categorical_accuracy: 0.7745 - val_one_hot_io_u: 0.5883
+Epoch 16/30
+265/266 [============================>.] - ETA: 0s - loss: 0.6859 - precision: 0.8126 - recall: 0.7015 - categorical_accuracy: 0.7598 - one_hot_io_u: 0.5653
+Epoch 16: val_loss did not improve from 0.64675
+266/266 [==============================] - 24s 90ms/step - loss: 0.6850 - precision: 0.8129 - recall: 0.7020 - categorical_accuracy: 0.7602 - one_hot_io_u: 0.5664 - val_loss: 0.6541 - val_precision: 0.8152 - val_recall: 0.7284 - val_categorical_accuracy: 0.7720 - val_one_hot_io_u: 0.5856
+Epoch 17/30
+264/266 [============================>.] - ETA: 0s - loss: 0.6713 - precision: 0.8203 - recall: 0.7104 - categorical_accuracy: 0.7682 - one_hot_io_u: 0.5777
+Epoch 17: val_loss did not improve from 0.64675
+266/266 [==============================] - 14s 54ms/step - loss: 0.6715 - precision: 0.8205 - recall: 0.7103 - categorical_accuracy: 0.7683 - one_hot_io_u: 0.5778 - val_loss: 0.6520 - val_precision: 0.8122 - val_recall: 0.7413 - val_categorical_accuracy: 0.7754 - val_one_hot_io_u: 0.5897
+Epoch 18/30
+261/266 [============================>.] - ETA: 0s - loss: 0.6766 - precision: 0.8158 - recall: 0.6999 - categorical_accuracy: 0.7611 - one_hot_io_u: 0.5662
+Epoch 18: val_loss did not improve from 0.64675
+266/266 [==============================] - 24s 91ms/step - loss: 0.6716 - precision: 0.8175 - recall: 0.7020 - categorical_accuracy: 0.7632 - one_hot_io_u: 0.5692 - val_loss: 0.6723 - val_precision: 0.8073 - val_recall: 0.7354 - val_categorical_accuracy: 0.7745 - val_one_hot_io_u: 0.5787
+Epoch 19/30
+266/266 [==============================] - ETA: 0s - loss: 0.6681 - precision: 0.8207 - recall: 0.7084 - categorical_accuracy: 0.7657 - one_hot_io_u: 0.5756
+Epoch 19: val_loss did not improve from 0.64675
+266/266 [==============================] - 14s 54ms/step - loss: 0.6681 - precision: 0.8207 - recall: 0.7084 - categorical_accuracy: 0.7657 - one_hot_io_u: 0.5756 - val_loss: 0.7133 - val_precision: 0.7844 - val_recall: 0.7250 - val_categorical_accuracy: 0.7558 - val_one_hot_io_u: 0.5393
+Epoch 20/30
+263/266 [============================>.] - ETA: 0s - loss: 0.6686 - precision: 0.8171 - recall: 0.7109 - categorical_accuracy: 0.7676 - one_hot_io_u: 0.5786
+Epoch 20: val_loss did not improve from 0.64675
+266/266 [==============================] - 15s 57ms/step - loss: 0.6676 - precision: 0.8176 - recall: 0.7113 - categorical_accuracy: 0.7680 - one_hot_io_u: 0.5788 - val_loss: 0.6900 - val_precision: 0.8031 - val_recall: 0.7429 - val_categorical_accuracy: 0.7725 - val_one_hot_io_u: 0.5750
+Epoch 21/30
+262/266 [============================>.] - ETA: 0s - loss: 0.6652 - precision: 0.8173 - recall: 0.7029 - categorical_accuracy: 0.7663 - one_hot_io_u: 0.5746
+Epoch 21: val_loss did not improve from 0.64675
+266/266 [==============================] - 23s 89ms/step - loss: 0.6622 - precision: 0.8185 - recall: 0.7044 - categorical_accuracy: 0.7676 - one_hot_io_u: 0.5755 - val_loss: 0.6969 - val_precision: 0.8025 - val_recall: 0.7354 - val_categorical_accuracy: 0.7700 - val_one_hot_io_u: 0.5710
+Epoch 22/30
+265/266 [============================>.] - ETA: 0s - loss: 0.6699 - precision: 0.8170 - recall: 0.7060 - categorical_accuracy: 0.7678 - one_hot_io_u: 0.5758
+Epoch 22: val_loss did not improve from 0.64675
+266/266 [==============================] - 17s 63ms/step - loss: 0.6699 - precision: 0.8172 - recall: 0.7063 - categorical_accuracy: 0.7679 - one_hot_io_u: 0.5761 - val_loss: 0.7111 - val_precision: 0.8088 - val_recall: 0.7354 - val_categorical_accuracy: 0.7708 - val_one_hot_io_u: 0.5744
+Epoch 23/30
+262/266 [============================>.] - ETA: 0s - loss: 0.6639 - precision: 0.8248 - recall: 0.7085 - categorical_accuracy: 0.7693 - one_hot_io_u: 0.5784
+Epoch 23: val_loss did not improve from 0.64675
+266/266 [==============================] - 23s 87ms/step - loss: 0.6620 - precision: 0.8255 - recall: 0.7099 - categorical_accuracy: 0.7703 - one_hot_io_u: 0.5791 - val_loss: 0.7080 - val_precision: 0.7946 - val_recall: 0.7500 - val_categorical_accuracy: 0.7712 - val_one_hot_io_u: 0.5708
+Epoch 24/30
+262/266 [============================>.] - ETA: 0s - loss: 0.6667 - precision: 0.8214 - recall: 0.7093 - categorical_accuracy: 0.7672 - one_hot_io_u: 0.5761
+Epoch 24: val_loss did not improve from 0.64675
+266/266 [==============================] - 24s 90ms/step - loss: 0.6651 - precision: 0.8218 - recall: 0.7104 - categorical_accuracy: 0.7681 - one_hot_io_u: 0.5768 - val_loss: 0.7125 - val_precision: 0.7986 - val_recall: 0.7371 - val_categorical_accuracy: 0.7687 - val_one_hot_io_u: 0.5655
+Epoch 25/30
+266/266 [==============================] - ETA: 0s - loss: 0.6620 - precision: 0.8192 - recall: 0.7146 - categorical_accuracy: 0.7716 - one_hot_io_u: 0.5834
+Epoch 25: val_loss did not improve from 0.64675
+266/266 [==============================] - 24s 90ms/step - loss: 0.6620 - precision: 0.8192 - recall: 0.7146 - categorical_accuracy: 0.7716 - one_hot_io_u: 0.5834 - val_loss: 0.7170 - val_precision: 0.7933 - val_recall: 0.7454 - val_categorical_accuracy: 0.7612 - val_one_hot_io_u: 0.5481
+Epoch 26/30
+264/266 [============================>.] - ETA: 0s - loss: 0.6630 - precision: 0.8221 - recall: 0.7129 - categorical_accuracy: 0.7707 - one_hot_io_u: 0.5811
+Epoch 26: val_loss did not improve from 0.64675
+266/266 [==============================] - 14s 55ms/step - loss: 0.6636 - precision: 0.8220 - recall: 0.7131 - categorical_accuracy: 0.7708 - one_hot_io_u: 0.5811 - val_loss: 0.6966 - val_precision: 0.7934 - val_recall: 0.7446 - val_categorical_accuracy: 0.7675 - val_one_hot_io_u: 0.5624
+Epoch 27/30
+266/266 [==============================] - ETA: 0s - loss: 0.6562 - precision: 0.8221 - recall: 0.7137 - categorical_accuracy: 0.7681 - one_hot_io_u: 0.5774
+Epoch 27: val_loss did not improve from 0.64675
+266/266 [==============================] - 24s 91ms/step - loss: 0.6562 - precision: 0.8221 - recall: 0.7137 - categorical_accuracy: 0.7681 - one_hot_io_u: 0.5774 - val_loss: 0.6592 - val_precision: 0.8102 - val_recall: 0.7388 - val_categorical_accuracy: 0.7750 - val_one_hot_io_u: 0.5840
+Epoch 28/30
+264/266 [============================>.] - ETA: 0s - loss: 0.6545 - precision: 0.8258 - recall: 0.7097 - categorical_accuracy: 0.7717 - one_hot_io_u: 0.5830
+Epoch 28: val_loss did not improve from 0.64675
+266/266 [==============================] - 14s 54ms/step - loss: 0.6543 - precision: 0.8259 - recall: 0.7099 - categorical_accuracy: 0.7720 - one_hot_io_u: 0.5832 - val_loss: 0.6637 - val_precision: 0.8178 - val_recall: 0.7359 - val_categorical_accuracy: 0.7766 - val_one_hot_io_u: 0.5848
+Epoch 29/30
+264/266 [============================>.] - ETA: 0s - loss: 0.6576 - precision: 0.8259 - recall: 0.7109 - categorical_accuracy: 0.7687 - one_hot_io_u: 0.5800
+Epoch 29: val_loss did not improve from 0.64675
+266/266 [==============================] - 16s 61ms/step - loss: 0.6561 - precision: 0.8265 - recall: 0.7112 - categorical_accuracy: 0.7689 - one_hot_io_u: 0.5802 - val_loss: 0.6871 - val_precision: 0.7955 - val_recall: 0.7329 - val_categorical_accuracy: 0.7633 - val_one_hot_io_u: 0.5514
+Epoch 30/30
+266/266 [==============================] - ETA: 0s - loss: 0.6533 - precision: 0.8234 - recall: 0.7118 - categorical_accuracy: 0.7696 - one_hot_io_u: 0.5826
+Epoch 30: val_loss did not improve from 0.64675
+266/266 [==============================] - 15s 56ms/step - loss: 0.6533 - precision: 0.8234 - recall: 0.7118 - categorical_accuracy: 0.7696 - one_hot_io_u: 0.5826 - val_loss: 0.6730 - val_precision: 0.8023 - val_recall: 0.7359 - val_categorical_accuracy: 0.7683 - val_one_hot_io_u: 0.5576
+****************************************************************************
+****************************** evaluating model... *************************
+************************************************
+************************************************
+Validation
+1219/1219 [==============================] - 7s 6ms/step - loss: 0.6585 - precision: 0.8038 - recall: 0.7293 - categorical_accuracy: 0.7736 - one_hot_io_u: 0.5689
+loss: 0.6584734320640564
+precision: 0.8037974834442139
+recall: 0.7292863130569458
+categorical_accuracy: 0.7735849022865295
+one_hot_io_u: 0.5688682794570923
+
+
+****************************************************************************
+****************************** saving parameters... ************************
+****************************************************************************
+*************** saving model config and history object... ******************
+****************************************************************************
+****************************** saving plots... *****************************
+Saving plots and model visualization at /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1...
+****************************************************************************
+****************************** saving models... ****************************
+****************************************************************************
+
+
+
+
+
+
Save the config file
+
+
+
+drive_config_file = Path(dnn_config.MODEL_DIR / f"{str(config_file).split('/')[-1]}")
+
+# Create the target directory if it doesn't exist
+drive_config_file.parent.mkdir(parents=True, exist_ok=True)
+
+# Copy the file
+shutil.copy(config_file, drive_config_file)
+
+print(f"File copied from {config_file} to {drive_config_file}")
+
+File copied from servir-aces/config.env to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/config.envLoad the logs files via TensorBoard
+
+
+
+log_dir_dnn = f"{str(dnn_config.MODEL_DIR)}/logs"
+log_dir_dnn
+
+'/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/logs'
+
+
+%tensorboard --logdir "{log_dir_dnn}"
+
+
+Load the Saved DNN Model
+
+
+
+dnn_model = tf.keras.models.load_model(f"{str(dnn_config.MODEL_DIR)}/trained-model")
+
+
+print(dnn_model.summary())
+
+Model: "model"
+_________________________________________________________________
+ Layer (type) Output Shape Param #
+=================================================================
+ input_layer (InputLayer) [(None, None, 8)] 0
+
+ dense (Dense) (None, None, 256) 2304
+
+ dropout (Dropout) (None, None, 256) 0
+
+ dense_1 (Dense) (None, None, 128) 32896
+
+ dropout_1 (Dropout) (None, None, 128) 0
+
+ dense_2 (Dense) (None, None, 64) 8256
+
+ dropout_2 (Dropout) (None, None, 64) 0
+
+ dense_3 (Dense) (None, None, 32) 2080
+
+ dropout_3 (Dropout) (None, None, 32) 0
+
+ dense_4 (Dense) (None, None, 5) 165
+
+=================================================================
+Total params: 45701 (178.52 KB)
+Trainable params: 45701 (178.52 KB)
+Non-trainable params: 0 (0.00 Byte)
+_________________________________________________________________
+NoneInference using Saved DNN Model
+Now we can use the saved model to start the export of the prediction of the image. For prediction, you would need to first prepare your image data. We have already exported the image needed here, which we will use for now. See this notebook to understand how we did it.
+In addition, this notebook shows how you can then use the image to predict from the saved Model.
+In any case, you now have the prediction in the Earth Engine as image.
+Independent Validation
+For independent validation, we will use a file that we have prepared. These files were collected using Collect Earth Online by SCO and NASA DEVELOP interns. We will be using GEE here. Before we do that, let’s make changes in our config file.
+We will make sure our GCS_PROJECT is setup correctly.
GCS_PROJECT = "servir-ee"Update the config file
+
+
+
+GCS_PROJECT = "servir-ee" # @param {type:"string"}
+
+
+config_settings = {
+ "GCS_PROJECT": GCS_PROJECT,
+}
+
+
+for config_key in config_settings:
+ dotenv.set_key(dotenv_path=config_file,
+ key_to_set=config_key,
+ value_to_set=config_settings[config_key]
+ )Load config file variable
+
+
+
+config = Config(config_file=config_file, override=True)
+
+BASEDIR: /content
+DATADIR: /content/datasets/dnn_planet_wo_indices
+using features: ['red_before', 'green_before', 'blue_before', 'nir_before', 'red_during', 'green_during', 'blue_during', 'nir_during']
+using labels: ['class']Import earthengine and geemap for visualization
+
+
+
+# Import, authenticate and initialize the Earth Engine library.
+import ee
+ee.Authenticate()
+EEUtils.initialize_session(use_highvolume=True, project=config.GCS_PROJECT)
+
+
+import geemap
+
+Map = geemap.Map()Class Information and Masking
+
+
+
+# CLASS
+# 0 - cropland etc.
+# 1 - rice
+# 2 - forest
+# 3 - Built up
+# 4 - Others (includes water body)
+l1 = ee.FeatureCollection("projects/servir-sco-assets/assets/Bhutan/BT_Admin_1")
+paro = l1.filter(ee.Filter.eq("ADM1_EN", "Paro"))
+
+
+# mask the rice growing zone
+# in Paro, rice grows upto 2600 m asl (double check to make sure??)
+dem = ee.Image("MERIT/DEM/v1_0_3") # ee.Image('USGS/SRTMGL1_003')
+dem = dem.clip(paro)
+rice_zone = dem.gte(0).And(dem.lte(2600))
+
+
+
+
+Model: U-Net
+Load and visualize the prediction output
+
+
+
+UNET_RGBN = ee.Image("projects/servir-ee/assets/dl-book/chapter-1/prediction/prediction_unet_v1")
+UNET_RGBN = UNET_RGBN.updateMask(rice_zone)
+Map.centerObject(UNET_RGBN, 11)
+Map.addLayer(UNET_RGBN.clip(paro), {"bands": ["prediction"], "min":0, "max":4, "palette": ["FFFF00", "FFC0CB", "267300", "E60000", "005CE6"]}, "UNET_RGBN")
+Map
+
+
+
+
+
+
+
+Calculate classification metrics
+Remapping to rice and non-rice output
+
+
+
+UNET_RGBN_remapped = UNET_RGBN.remap([0, 1, 2, 3, 4], [0, 1, 0, 0, 0], 0, "prediction")
+Map.addLayer(UNET_RGBN_remapped, {"min": 0, "max": 1, "palette": ["cfcf00", "267300"]}, "UNET_RGBN_remapped")
+Map
+
+
+
+
+
+
+
+
+
+
+sampling_geom = ee.FeatureCollection("projects/servir-ee/assets/dl-book/chapter-1/data/sampledGeom")
+ceo_final_data = ee.FeatureCollection("projects/servir-ee/assets/dl-book/chapter-1/data/ceoData")
+ceo_final_data = ee.FeatureCollection(ceo_final_data.filter(ee.Filter.bounds(sampling_geom).Not()))
+
+
+
+
+
+
+
+prediction_unet = UNET_RGBN_remapped.sampleRegions(
+ collection = ceo_final_data,
+ scale = 10,
+ geometries = True
+)
+
+# print("predictionOutputUnet", prediction_unet.getInfo())
+
+
+
+
+
+
+
+error_matrix_unet = prediction_unet.errorMatrix(actual="rice", predicted="remapped")
+test_acc_unet = error_matrix_unet.accuracy()
+test_kappa_unet = error_matrix_unet.kappa()
+test_recall_producer_acc_unet = error_matrix_unet.producersAccuracy().get([1, 0])
+test_precision_consumer_acc_unet = error_matrix_unet.consumersAccuracy().get([0, 1])
+f1_unet = error_matrix_unet.fscore().get([1])
+
+
+
+
+
+
+
+print("error_matrix_unet", error_matrix_unet.getInfo())
+print("test_acc_unet", test_acc_unet.getInfo())
+print("test_kappa_unet", test_kappa_unet.getInfo())
+print("test_recall_producer_acc_unet", test_recall_producer_acc_unet.getInfo())
+print("test_precision_consumer_acc_unet", test_precision_consumer_acc_unet.getInfo())
+print("f1_unet", f1_unet.getInfo())
+
+
+
+
+
+
+error_matrix_unet [[1191, 29], [33, 50]]
+test_acc_unet 0.9524174980813507
+test_kappa_unet 0.5919321924312524
+test_recall_producer_acc_unet 0.6024096385542169
+test_precision_consumer_acc_unet 0.6329113924050633
+f1_unet 0.6172839506172839Calculate Probability Distribution
+
+
+
+prob_output_unet = UNET_RGBN.select(["prediction", "others_etc", "cropland_etc", "urban", "forest", "rice"]) \
+ .rename(["prediction_class", "others_prob", "cropland_prob", "urban_prob", "forest_prob", "rice_prob"]) \
+ .sampleRegions(collection=ceo_final_data, scale=10, geometries=True)
+
+# print("prob_output_unet", prob_output_unet.getInfo())
+
+
+
+
+
+
+
+prob_output_unet = prob_output_unet.getInfo()
+
+
+
+
+Model: DNN
+Load and visualize the prediction output
+
+
+
+DNN_RGBN = ee.Image("projects/servir-ee/assets/dl-book/chapter-1/prediction/prediction_dnn_v1")
+DNN_RGBN = DNN_RGBN.updateMask(rice_zone)
+Map.centerObject(DNN_RGBN)
+Map.addLayer(DNN_RGBN.clip(paro), {"bands": ["prediction"], "min":0, "max":4, "palette": ["FFFF00", "FFC0CB", "267300", "E60000", "005CE6"]}, "DNN_RGBN")
+Map
+
+
+
+
+
+
+
+Calculate classification metrics
+
+
+
+DNN_RGBN_remapped = DNN_RGBN.remap([0, 1, 2, 3, 4], [0, 1, 0, 0, 0], 0, "prediction")
+Map.addLayer(DNN_RGBN_remapped, {"min": 0, "max": 1, "palette": ["cfcf00", "267300"]}, "DNN_RGBN_remapped")
+Map
+
+
+
+
+
+
+
+
+
+
+prediction_dnn = DNN_RGBN_remapped.sampleRegions(
+ collection = ceo_final_data,
+ scale = 10,
+ geometries = True
+)
+
+# print("predictionOutputDNN", prediction_dnn.getInfo())
+
+
+
+
+
+
+
+error_matrix_dnn = prediction_dnn.errorMatrix(actual="rice", predicted="remapped")
+test_acc_dnn = error_matrix_dnn.accuracy()
+test_kappa_dnn = error_matrix_dnn.kappa()
+test_recall_producer_acc_dnn = error_matrix_dnn.producersAccuracy().get([1, 0])
+test_precision_consumer_acc_dnn = error_matrix_dnn.consumersAccuracy().get([0, 1])
+f1_dnn = error_matrix_dnn.fscore().get([1])
+
+
+
+
+
+
+
+print("error_matrix_dnn", error_matrix_dnn.getInfo())
+print("test_acc_dnn", test_acc_dnn.getInfo())
+print("test_kappa_dnn", test_kappa_dnn.getInfo())
+print("test_recall_producer_acc_dnn", test_recall_producer_acc_dnn.getInfo())
+print("test_precision_consumer_acc_dnn", test_precision_consumer_acc_dnn.getInfo())
+print("f1_dnn", f1_dnn.getInfo())
+
+
+
+
+
+
+error_matrix_dnn [[1175, 45], [20, 63]]
+test_acc_dnn 0.9501151189562548
+test_kappa_dnn 0.6332676611314382
+test_recall_producer_acc_dnn 0.7590361445783133
+test_precision_consumer_acc_dnn 0.5833333333333334
+f1_dnn 0.6596858638743456Calculate Probability Distribution
+
+
+
+prob_output_dnn = DNN_RGBN.select(["prediction", "others_etc", "cropland_etc", "urban", "forest", "rice"]) \
+ .rename(["prediction_class", "others_prob", "cropland_prob", "urban_prob", "forest_prob", "rice_prob"]) \
+ .sampleRegions(collection=ceo_final_data, scale=10, geometries=True)
+
+# print("prob_output_dnn", prob_output_dnn.getInfo())
+
+
+
+
+
+
+
+prob_output_dnn = prob_output_dnn.getInfo()
+
+
+
+
+Figures and Plots
+Training and Validation Plot
+
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import pickle
+
+%matplotlib inline
+
+
+
+
+
+
+
+with open(unet_config.MODEL_DIR / "model.pkl", "rb") as f:
+ unet_model_metrics = pickle.load(f)
+
+with open(dnn_config.MODEL_DIR / "model.pkl", "rb") as f:
+ dnn_model_metrics = pickle.load(f)
+
+
+
+
+
+
+
+# Create subplots for different metrics in a 3x4 grid
+fig, axs = plt.subplots(2, 4, figsize=(4*7, 6*2))
+
+colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"]
+metrics = ["loss", "precision", "recall", "categorical_accuracy"]
+metrics_name = ["Loss", "Precision", "Recall", "Categorical Accuracy"]
+
+epochs = range(1, config.EPOCHS + 1)
+
+title_fontsize = 22
+label_fontsize = 22
+legend_fontsize = 15
+tick_fontsize = 18
+lw=1.5
+
+for i in range(2):
+ for y in range(len(metrics)):
+ if i == 1:
+ axs[i][y].plot(epochs, unet_model_metrics[f"val_{metrics[y]}"], color=colors[0], marker="o", lw=lw, label=f"U-Net validate {metrics[y]}")
+ axs[i][y].plot(epochs, dnn_model_metrics[f"val_{metrics[y]}"], color=colors[1], lw=lw, marker="o", label=f"DNN validate {metrics[y]}")
+ axs[i][y].set_title(f"Validate {metrics_name[y]}", fontsize=title_fontsize)
+ axs[i][y].set_xlabel("epochs", fontsize=label_fontsize)
+ axs[i][y].set_ylabel(f"{metrics[y]}", fontsize=label_fontsize)
+ axs[i][y].grid(linestyle="dotted", alpha=0.7)
+ axs[i][y].legend(fontsize=legend_fontsize)
+ axs[i][y].tick_params(axis="both", which="major", labelsize=tick_fontsize)
+ else:
+ axs[i][y].plot(epochs, unet_model_metrics[metrics[y]], color=colors[0], lw=lw, marker="o", label=f"U-Net train {metrics[y]}")
+ axs[i][y].plot(epochs, dnn_model_metrics[metrics[y]], color=colors[1], lw=lw, marker="o", label=f"DNN train {metrics[y]}")
+ axs[i][y].set_title(f"Train {metrics_name[y]}", fontsize=title_fontsize)
+ axs[i][y].set_xlabel("epochs", fontsize=label_fontsize)
+ axs[i][y].set_ylabel(f"{metrics[y]}", fontsize=label_fontsize)
+ axs[i][y].grid(linestyle="dotted", alpha=0.7)
+ axs[i][y].legend(fontsize=legend_fontsize)
+ axs[i][y].tick_params(axis="both", which="major", labelsize=tick_fontsize)
+
+
+# Adjust layout and show the plot
+plt.tight_layout()
+# plt.savefig("metrics_plot_model_comparison.png", dpi=500, bbox_inches="tight")
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+# Create subplots for different metrics in a 3x4 grid
+fig, axs = plt.subplots(1, 4, figsize=(4*7, 6*1))
+
+colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"]
+metrics = ["loss", "precision", "recall", "categorical_accuracy"]
+metrics_name = ["Loss", "Precision", "Recall", "Categorical Accuracy"]
+
+epochs = range(1, config.EPOCHS + 1)
+
+title_fontsize = 22
+label_fontsize = 22
+legend_fontsize = 15
+tick_fontsize = 18
+lw=1.5
+
+for y in range(len(metrics)):
+ axs[y].plot(epochs, unet_model_metrics[f"val_{metrics[y]}"], color=colors[0], marker="o", lw=lw, label=f"U-Net validate {metrics[y]}")
+ axs[y].plot(epochs, dnn_model_metrics[f"val_{metrics[y]}"], color=colors[1], lw=lw, marker="o", label=f"DNN validate {metrics[y]}")
+
+ axs[y].plot(epochs, unet_model_metrics[metrics[y]], color=colors[2], lw=lw, marker="o", label=f"U-Net train {metrics[y]}")
+ axs[y].plot(epochs, dnn_model_metrics[metrics[y]], color=colors[3], lw=lw, marker="o", label=f"DNN train {metrics[y]}")
+ axs[y].set_title(f"{metrics_name[y]}", fontsize=title_fontsize)
+ axs[y].set_xlabel("epochs", fontsize=label_fontsize)
+ axs[y].set_ylabel(f"{metrics[y]}", fontsize=label_fontsize)
+ axs[y].grid(linestyle="dotted", alpha=0.7)
+ axs[y].legend(fontsize=legend_fontsize)
+ axs[y].tick_params(axis="both", which="major", labelsize=tick_fontsize)
+
+
+# Adjust layout and show the plot
+plt.tight_layout()
+# plt.savefig("metrics_plot_model_comparison.png", dpi=500, bbox_inches="tight")
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
Probability Distribution Plot
+
+
+
+all_data = {}
+
+unet_data = []
+dnn_data = []
+
+unet_rice_data = []
+dnn_rice_data = []
+
+unet_other_data = []
+dnn_other_data = []
+
+for i, feature in enumerate(prob_output_unet["features"]):
+ unet_rice_prob = round(feature["properties"]["rice_prob"], 5)
+ unet_other_prob = round(feature["properties"]["cropland_prob"] + round(feature["properties"]["forest_prob"] + feature["properties"]["others_prob"]+ feature["properties"]["urban_prob"]), 5)
+ unet_data.append([unet_rice_prob, unet_other_prob])
+ unet_rice_data.append(unet_rice_prob)
+ unet_other_data.append(unet_other_prob)
+
+ dnn_feature = prob_output_dnn["features"][i]
+ dnn_rice_prob = round(dnn_feature["properties"]["rice_prob"], 5)
+ dnn_other_prob = 1. - round(dnn_feature["properties"]["rice_prob"], 5)
+ # dnn_other_prob = round(dnn_feature["properties"]["cropland_prob"] + dnn_feature["properties"]["forest_prob"] + dnn_feature["properties"]["others_prob"]+ dnn_feature["properties"]["urban_prob"], 5)
+ dnn_data.append([dnn_rice_prob, dnn_other_prob])
+ dnn_rice_data.append(dnn_rice_prob)
+ dnn_other_data.append(dnn_other_prob)
+
+
+
+
+
+
+
+
+
+fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))
+
+title_fontsize = 22
+label_fontsize = 10
+tick_fontsize = 10
+
+# rectangular box plot
+bplot1 = ax1.boxplot([unet_rice_data, dnn_rice_data],
+ notch=True,
+ vert=True, # vertical box alignment
+ patch_artist=True, # fill with color
+ labels=["U-Net", "DNN"],
+ sym="k+") # will be used to label x-ticks
+ax1.set_title("Rice Probability")
+
+# notch shape box plot
+bplot2 = ax2.boxplot([unet_other_data, dnn_other_data],
+ notch=True, # notch shape
+ vert=True, # vertical box alignment
+ patch_artist=True, # fill with color
+ labels=["U-Net", "DNN"],
+ sym="k+") # will be used to label x-ticks
+ax2.set_title("Other Probability")
+
+
+
+
+
+
+Text(0.5, 1.0, 'Other Probability')
+
+
+
+
+
+
