From 3d6b2065b65bcf6af2f2999eb7597c99f6a1be30 Mon Sep 17 00:00:00 2001
From: SurajSSingh <surajss@uci.edu>
Date: Wed, 11 May 2022 20:41:37 -0700
Subject: [PATCH] Finished creating multiple model
---
.gitignore | 3 +-
.history/attention.csv | 21 +
.history/baseline.csv | 19 +
.history/baseline_prev.csv | 2 +
.history/gru.csv | 20 +
.history/lstm.csv | 21 +
.model/attention/keras_metadata.pb | 15 +
.model/attention/saved_model.pb | Bin 0 -> 139289 bytes
.../variables/variables.data-00000-of-00001 | Bin 0 -> 12644 bytes
.model/attention/variables/variables.index | Bin 0 -> 1599 bytes
.model/attention_old/keras_metadata.pb | 15 +
.model/attention_old/saved_model.pb | Bin 0 -> 139289 bytes
.../variables/variables.data-00000-of-00001 | Bin 0 -> 12644 bytes
.../attention_old/variables/variables.index | Bin 0 -> 1599 bytes
.model/baseline/keras_metadata.pb | 9 +
.model/baseline/saved_model.pb | Bin 0 -> 89440 bytes
.../variables/variables.data-00000-of-00001 | Bin 0 -> 14711 bytes
.model/baseline/variables/variables.index | Bin 0 -> 1606 bytes
.model/baseline_old/keras_metadata.pb | 9 +
.model/baseline_old/saved_model.pb | Bin 0 -> 89440 bytes
.../variables/variables.data-00000-of-00001 | Bin 0 -> 14711 bytes
.model/baseline_old/variables/variables.index | Bin 0 -> 1606 bytes
.model/gru/keras_metadata.pb | 10 +
.model/gru/saved_model.pb | Bin 0 -> 641185 bytes
.../variables/variables.data-00000-of-00001 | Bin 0 -> 21165 bytes
.model/gru/variables/variables.index | Bin 0 -> 1786 bytes
.model/gru_old/keras_metadata.pb | 10 +
.model/gru_old/saved_model.pb | Bin 0 -> 641185 bytes
.../variables/variables.data-00000-of-00001 | Bin 0 -> 21165 bytes
.model/gru_old/variables/variables.index | Bin 0 -> 1786 bytes
.model/lstm/keras_metadata.pb | 10 +
.model/lstm/saved_model.pb | Bin 0 -> 737286 bytes
.../variables/variables.data-00000-of-00001 | Bin 0 -> 25235 bytes
.model/lstm/variables/variables.index | Bin 0 -> 1776 bytes
.model/lstm_old/keras_metadata.pb | 10 +
.model/lstm_old/saved_model.pb | Bin 0 -> 737286 bytes
.../variables/variables.data-00000-of-00001 | Bin 0 -> 25235 bytes
.model/lstm_old/variables/variables.index | Bin 0 -> 1776 bytes
attention_model.py | 21 +
baseline_model.py | 25 +
common.py | 113 ++++
gru_model.py | 20 +
lstm_model.py | 22 +
simple_alarm_clock.py | 96 +--
smart_alarm_clock.py | 0
tf_model.ipynb | 581 +++++++++++-------
46 files changed, 805 insertions(+), 247 deletions(-)
create mode 100755 .history/attention.csv
create mode 100755 .history/baseline.csv
create mode 100644 .history/baseline_prev.csv
create mode 100755 .history/gru.csv
create mode 100755 .history/lstm.csv
create mode 100755 .model/attention/keras_metadata.pb
create mode 100755 .model/attention/saved_model.pb
create mode 100755 .model/attention/variables/variables.data-00000-of-00001
create mode 100755 .model/attention/variables/variables.index
create mode 100755 .model/attention_old/keras_metadata.pb
create mode 100755 .model/attention_old/saved_model.pb
create mode 100755 .model/attention_old/variables/variables.data-00000-of-00001
create mode 100755 .model/attention_old/variables/variables.index
create mode 100755 .model/baseline/keras_metadata.pb
create mode 100755 .model/baseline/saved_model.pb
create mode 100755 .model/baseline/variables/variables.data-00000-of-00001
create mode 100755 .model/baseline/variables/variables.index
create mode 100644 .model/baseline_old/keras_metadata.pb
create mode 100644 .model/baseline_old/saved_model.pb
create mode 100644 .model/baseline_old/variables/variables.data-00000-of-00001
create mode 100644 .model/baseline_old/variables/variables.index
create mode 100755 .model/gru/keras_metadata.pb
create mode 100755 .model/gru/saved_model.pb
create mode 100755 .model/gru/variables/variables.data-00000-of-00001
create mode 100755 .model/gru/variables/variables.index
create mode 100755 .model/gru_old/keras_metadata.pb
create mode 100755 .model/gru_old/saved_model.pb
create mode 100755 .model/gru_old/variables/variables.data-00000-of-00001
create mode 100755 .model/gru_old/variables/variables.index
create mode 100755 .model/lstm/keras_metadata.pb
create mode 100755 .model/lstm/saved_model.pb
create mode 100755 .model/lstm/variables/variables.data-00000-of-00001
create mode 100755 .model/lstm/variables/variables.index
create mode 100755 .model/lstm_old/keras_metadata.pb
create mode 100755 .model/lstm_old/saved_model.pb
create mode 100755 .model/lstm_old/variables/variables.data-00000-of-00001
create mode 100755 .model/lstm_old/variables/variables.index
create mode 100755 attention_model.py
create mode 100755 baseline_model.py
create mode 100644 common.py
create mode 100755 gru_model.py
create mode 100755 lstm_model.py
create mode 100644 smart_alarm_clock.py
diff --git a/.gitignore b/.gitignore
index 2269427..988c81e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
*.pdf
.data/
-*.pyc
\ No newline at end of file
+*.pyc
+colab_copy.ipynb
diff --git a/.history/attention.csv b/.history/attention.csv
new file mode 100755
index 0000000..a4fd104
--- /dev/null
+++ b/.history/attention.csv
@@ -0,0 +1,21 @@
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diff --git a/.history/baseline.csv b/.history/baseline.csv
new file mode 100755
index 0000000..868e84b
--- /dev/null
+++ b/.history/baseline.csv
@@ -0,0 +1,19 @@
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diff --git a/.history/baseline_prev.csv b/.history/baseline_prev.csv
new file mode 100644
index 0000000..00f38e1
--- /dev/null
+++ b/.history/baseline_prev.csv
@@ -0,0 +1,2 @@
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diff --git a/.history/gru.csv b/.history/gru.csv
new file mode 100755
index 0000000..ffc90be
--- /dev/null
+++ b/.history/gru.csv
@@ -0,0 +1,20 @@
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diff --git a/.history/lstm.csv b/.history/lstm.csv
new file mode 100755
index 0000000..faf23f3
--- /dev/null
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diff --git a/.model/attention/variables/variables.index b/.model/attention/variables/variables.index
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diff --git a/.model/attention_old/keras_metadata.pb b/.model/attention_old/keras_metadata.pb
new file mode 100755
index 0000000..a85a548
--- /dev/null
+++ b/.model/attention_old/keras_metadata.pb
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new file mode 100755
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GIT binary patch
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diff --git a/.model/baseline/keras_metadata.pb b/.model/baseline/keras_metadata.pb
new file mode 100755
index 0000000..f5bbc8a
--- /dev/null
+++ b/.model/baseline/keras_metadata.pb
@@ -0,0 +1,9 @@
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diff --git a/.model/baseline_old/keras_metadata.pb b/.model/baseline_old/keras_metadata.pb
new file mode 100644
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+Ë#root"_tf_keras_sequential*¦#{"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 10, 7]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 16, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 4, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "shared_object_id": 8, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 10, 7]}, "ndim": 3, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 7]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 10, 7]}, "float32", "input_1"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 10, 7]}, "float32", "input_1"]}, "keras_version": "2.8.0", "backend": "tensorflow", "model_config": {"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 10, 7]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "shared_object_id": 0}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 16, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 3}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "shared_object_id": 4}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 4, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 5}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 6}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 7}]}}, "training_config": {"loss": {"class_name": "CategoricalCrossentropy", "config": {"reduction": "auto", "name": "categorical_crossentropy", "from_logits": true, "label_smoothing": 0.0, "axis": -1}, "shared_object_id": 10}, "metrics": [[{"class_name": "CategoricalCrossentropy", "config": {"name": "categorical_crossentropy", "dtype": "float32", "from_logits": true, "label_smoothing": 0}, "shared_object_id": 11}, {"class_name": "CategoricalAccuracy", "config": {"name": "categorical_accuracy", "dtype": "float32"}, "shared_object_id": 12}, {"class_name": "CategoricalHinge", "config": {"name": "categorical_hinge", "dtype": "float32"}, "shared_object_id": 13}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "Adam", "config": {"name": "Adam", "learning_rate": 0.0010000000474974513, "decay": 0.0, "beta_1": 0.8999999761581421, "beta_2": 0.9990000128746033, "epsilon": 1e-07, "amsgrad": false}}}}2
+Åroot.layer_with_weights-0"_tf_keras_layer*Ž{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 16, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 3, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 7}}, "shared_object_id": 14}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 7]}}2
+’ root.layer-1"_tf_keras_layer*è{"name": "flatten", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "shared_object_id": 4, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 15}}2
+Èroot.layer_with_weights-1"_tf_keras_layer*‘{"name": "dense_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 4, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 5}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 6}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 7, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 160}}, "shared_object_id": 16}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 160]}}2
+¹=root.keras_api.metrics.0"_tf_keras_metric*‚{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 17}2
+Ÿ>root.keras_api.metrics.1"_tf_keras_metric*è{"class_name": "CategoricalCrossentropy", "name": "categorical_crossentropy", "dtype": "float32", "config": {"name": "categorical_crossentropy", "dtype": "float32", "from_logits": true, "label_smoothing": 0}, "shared_object_id": 11}2
+è?root.keras_api.metrics.2"_tf_keras_metric*±{"class_name": "CategoricalAccuracy", "name": "categorical_accuracy", "dtype": "float32", "config": {"name": "categorical_accuracy", "dtype": "float32"}, "shared_object_id": 12}2
+ß@root.keras_api.metrics.3"_tf_keras_metric*¨{"class_name": "CategoricalHinge", "name": "categorical_hinge", "dtype": "float32", "config": {"name": "categorical_hinge", "dtype": "float32"}, "shared_object_id": 13}2
\ No newline at end of file
diff --git a/.model/baseline_old/saved_model.pb b/.model/baseline_old/saved_model.pb
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+Âroot.layer_with_weights-1"_tf_keras_layer*‹{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 4, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 6}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 7}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 8, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 16}}, "shared_object_id": 16}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16]}}2
+ß root.layer_with_weights-0.cell"_tf_keras_layer*£{"name": "gru_cell", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "GRUCell", "config": {"name": "gru_cell", "trainable": true, "dtype": "float32", "units": 16, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2, "reset_after": true}, "shared_object_id": 4}2
+¹=root.keras_api.metrics.0"_tf_keras_metric*‚{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 17}2
+Ÿ>root.keras_api.metrics.1"_tf_keras_metric*è{"class_name": "CategoricalCrossentropy", "name": "categorical_crossentropy", "dtype": "float32", "config": {"name": "categorical_crossentropy", "dtype": "float32", "from_logits": true, "label_smoothing": 0}, "shared_object_id": 12}2
+è?root.keras_api.metrics.2"_tf_keras_metric*±{"class_name": "CategoricalAccuracy", "name": "categorical_accuracy", "dtype": "float32", "config": {"name": "categorical_accuracy", "dtype": "float32"}, "shared_object_id": 13}2
+ß@root.keras_api.metrics.3"_tf_keras_metric*¨{"class_name": "CategoricalHinge", "name": "categorical_hinge", "dtype": "float32", "config": {"name": "categorical_hinge", "dtype": "float32"}, "shared_object_id": 14}2
\ No newline at end of file
diff --git a/.model/gru/saved_model.pb b/.model/gru/saved_model.pb
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diff --git a/.model/gru_old/keras_metadata.pb b/.model/gru_old/keras_metadata.pb
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diff --git a/.model/lstm/keras_metadata.pb b/.model/lstm/keras_metadata.pb
new file mode 100755
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diff --git a/.model/lstm_old/variables/variables.data-00000-of-00001 b/.model/lstm_old/variables/variables.data-00000-of-00001
new file mode 100755
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diff --git a/.model/lstm_old/variables/variables.index b/.model/lstm_old/variables/variables.index
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diff --git a/attention_model.py b/attention_model.py
new file mode 100755
index 0000000..f20fbfc
--- /dev/null
+++ b/attention_model.py
@@ -0,0 +1,21 @@
+from common import *
+from attention import Attention
+
+# Hyper-parameters
+ATTENTION_UNITS = 16
+
+# Model Definition
+def main(baseline=None):
+ am_model = keras.Sequential()
+ am_model.add(layers.Input(shape=(INPUT_TIME_STEP, INPUT_FEATURES_SIZE)))
+ am_model.add(Attention(ATTENTION_UNITS))
+ am_model.add(layers.Dense(SLEEP_STAGES))
+ am_model.build()
+ print(am_model.summary())
+ am_history = compile_and_fit(model=am_model, window=wg, baseline=baseline)
+ print("Finished training")
+ am_model.save(".model/attention")
+ save_history(am_history.history, ".history/attention.csv")
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/baseline_model.py b/baseline_model.py
new file mode 100755
index 0000000..1bbfb63
--- /dev/null
+++ b/baseline_model.py
@@ -0,0 +1,25 @@
+from common import *
+
+
+def main():
+ global baseline_model
+ BASELINE_UNITS = 16
+ baseline_model = keras.Sequential(
+ [
+ layers.Input(shape=(INPUT_TIME_STEP, INPUT_FEATURES_SIZE)),
+ layers.Dense(BASELINE_UNITS),
+ # layers.Dense(BASELINE_UNITS),
+ # layers.Dense(BASELINE_UNITS),
+ layers.Flatten(),
+ layers.Dense(SLEEP_STAGES),
+ ]
+ )
+ baseline_model.build()
+ print(baseline_model.summary())
+ baseline_history = compile_and_fit(baseline_model, wg)
+ print("Finished training")
+ save_history(baseline_history.history, ".history/baseline.csv")
+ baseline_model.save(".model/baseline")
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/common.py b/common.py
new file mode 100644
index 0000000..c3aca7b
--- /dev/null
+++ b/common.py
@@ -0,0 +1,113 @@
+import tensorflow as tf
+import numpy as np
+from tensorflow import keras
+from tensorflow.keras import layers
+import pandas as pd
+
+TEST_SIZE = 365*3//5
+VALIDATION_SIZE = 365*4//5
+
+BATCH_SIZE = 64
+INPUT_TIME_STEP = 10 # in minutes
+INPUT_FEATURES_SIZE = 7
+SLEEP_STAGES = 4
+MAX_EPOCHS = 20
+
+SLEEP_DATA_PATH = ".data/sleep_data_simple.csv"
+
+def training_test_split_by_unique_index(data, index: str, test_size: int = 10):
+ test_ids = np.random.choice(data[index].unique(), size = test_size, replace=False)
+ return data[~data[index].isin(test_ids)], data[data[index].isin(test_ids)]
+
+
+# Adapted from https://www.tensorflow.org/tutorials/structured_data/time_series
+class WindowGenerator():
+ def __init__(self, data, index: str = "sleep_id", input_width: int = INPUT_TIME_STEP, validation_size: int = VALIDATION_SIZE, test_size: int = TEST_SIZE, input_feature_slice: slice = slice(1,100), label_feature_slice: slice = slice(-4,100), generate_data_now: bool = True):
+ # Partition data
+ self.training, self.testing = training_test_split_by_unique_index(data, index, test_size)
+ self.training, self.validation = training_test_split_by_unique_index(self.training, index, validation_size)
+
+ # Window paramters
+ self.input_width = input_width
+ self.label_width = 1
+ self.shift = 1
+
+ self.total_window_size = self.input_width + self.shift
+
+ self.input_slice = slice(0, input_width)
+ self.input_indices = np.arange(self.total_window_size)[self.input_slice]
+
+ self.label_start = self.total_window_size - self.label_width
+ self.labels_slice = slice(self.label_start, None)
+ self.label_indices = np.arange(self.total_window_size)[self.labels_slice]
+
+ self.input_feature_slice = input_feature_slice
+ self.label_feature_slice = label_feature_slice
+
+ self.sample_ds = self.make_dataset(data[data[index] == 0])
+
+ if generate_data_now:
+ self.training_ds = self.make_dataset(self.training, index)
+ self.validation_ds = self.make_dataset(self.validation, index)
+ self.testing_ds = self.make_dataset(self.testing, index)
+
+
+ def __repr__(self):
+ return "WindowGenerator:\n\t" +'\n\t'.join([
+ f'Total window size: {self.total_window_size}',
+ f'Input indices: {self.input_indices}',
+ f'Label indices: {self.label_indices}',
+ ])
+
+ def split_window(self, features):
+ inputs = features[:, self.input_slice, self.input_feature_slice]
+ labels = tf.squeeze(features[:, self.labels_slice, self.label_feature_slice])
+ inputs.set_shape([None, self.input_width, None])
+ # labels.set_shape([None, self.label_width, None])
+ return inputs, labels
+
+ def make_dataset(self, data, index_group: str = "sleep_id", sort_by: str = "minutes_since_begin"):
+ ds_all = None
+ for i_group in data[index_group].unique():
+ subset_data = np.array(data[data[index_group] == i_group].sort_values(by=[sort_by]), dtype=np.float32)
+ ds = tf.keras.utils.timeseries_dataset_from_array(
+ data=subset_data,
+ targets=None,
+ sequence_length=self.total_window_size,
+ sequence_stride=1,
+ shuffle=False,
+ batch_size=BATCH_SIZE,)
+ ds_all = ds if ds_all is None else ds_all.concatenate(ds)
+ ds_all = ds_all.map(self.split_window)
+
+ return ds_all
+
+# Adapted from https://www.tensorflow.org/tutorials/structured_data/time_series#linear_model
+def compile_and_fit(model, window: WindowGenerator, loss = tf.losses.CategoricalCrossentropy(from_logits=True), optimizer = tf.optimizers.Adam(), metrics = None, early_stop: bool = True, patience:int = 5, baseline = None, epochs: int = MAX_EPOCHS):
+ if metrics is None:
+ metrics = [tf.keras.metrics.CategoricalCrossentropy(from_logits=True), tf.keras.metrics.CategoricalAccuracy(), tf.keras.metrics.CategoricalHinge()]
+
+ callbacks = []
+ if early_stop:
+ early_stopping = tf.keras.callbacks.EarlyStopping(
+ monitor='val_loss',
+ patience=patience,
+ baseline = baseline,
+ mode='min'
+ )
+ callbacks.append(early_stopping)
+
+ model.compile(
+ loss=loss,
+ optimizer=optimizer,
+ metrics=metrics,
+ )
+
+ return model.fit(window.training_ds, epochs=epochs, validation_data=window.validation_ds, callbacks=callbacks)
+
+def save_history(history, file_name: str = "history.csv"):
+ pd.DataFrame.from_dict(history).to_csv(file_name, index=False)
+
+
+sleep_data = pd.read_csv(SLEEP_DATA_PATH)
+wg = WindowGenerator(sleep_data)
diff --git a/gru_model.py b/gru_model.py
new file mode 100755
index 0000000..50b5382
--- /dev/null
+++ b/gru_model.py
@@ -0,0 +1,20 @@
+from common import *
+
+# Hyper-parameters
+GRU_UNITS = 16
+
+# Model Definition
+def main(baseline=None):
+ gru_model = keras.Sequential()
+ gru_model.add(layers.Input(shape=(INPUT_TIME_STEP, INPUT_FEATURES_SIZE)))
+ gru_model.add(layers.GRU(GRU_UNITS))
+ gru_model.add(layers.Dense(SLEEP_STAGES))
+ gru_model.build()
+ print(gru_model.summary())
+ gru_history = compile_and_fit(model=gru_model, window=wg, baseline=baseline)
+ print("Finished training")
+ gru_model.save(".model/gru")
+ save_history(gru_history.history, ".history/gru.csv")
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/lstm_model.py b/lstm_model.py
new file mode 100755
index 0000000..40eb965
--- /dev/null
+++ b/lstm_model.py
@@ -0,0 +1,22 @@
+from common import *
+
+# Hyper-parameters
+LSTM_UNITS = 16
+
+# Model Definition
+def main(baseline=None):
+ lstm_model = keras.Sequential()
+ lstm_model.add(layers.Input(shape=(INPUT_TIME_STEP, INPUT_FEATURES_SIZE)))
+ # lstm_model.add(layers.LSTM(LSTM_UNITS, stateful=False, return_sequences=True))
+ # lstm_model.add(layers.LSTM(LSTM_UNITS, stateful=False, return_sequences=True))
+ lstm_model.add(layers.LSTM(LSTM_UNITS, stateful=False, return_sequences=False))
+ lstm_model.add(layers.Dense(SLEEP_STAGES))
+ lstm_model.build()
+ print(lstm_model.summary())
+ lstm_history = compile_and_fit(model=lstm_model, window=wg, baseline=baseline)
+ print("Finished training")
+ lstm_model.save(".model/lstm")
+ save_history(lstm_history.history, ".history/lstm.csv")
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/simple_alarm_clock.py b/simple_alarm_clock.py
index 306dde8..e9c981e 100644
--- a/simple_alarm_clock.py
+++ b/simple_alarm_clock.py
@@ -3,7 +3,8 @@ from enum import Enum, auto
from datetime import time, datetime
from typing import Optional, Any
from time import sleep
-import vlc
+from pynput import keyboard
+from pynput.keyboard import Key, Controller
# Simple Alarm Clock has 5 functionalities:
# 1. Set Alarm Time - Set the time for the alarm to sound, does NOT make the alarm active
@@ -12,20 +13,23 @@ import vlc
# 4. Snooze Alarm - Stop the alarm sound and wait for some time to play the alarm again
# 5. Stop Alarm - If the alarm is active or is playing, stop the alarm (deactivate state)
-DEFAULT_VOLUME = 50
+ALARM_TIME = time(19, 13, 0)
+SNOOZE_SEC = 5
+SNOOZE_KEY = keyboard.Key.up
+ALARM_OFF_KEY = keyboard.Key.esc
class AlarmState(Enum):
SET = auto(),
- STARTED = auto(),
+ RUNNING = auto(),
PLAYING = auto(),
SNOOZED = auto(),
DEACTIVATED = auto(),
@dataclass
-class AlarmClock:
- player: Optional
+class BaseAlarmClock:
+ # player: Optional
wake_time: time
current_state: AlarmState = AlarmState.DEACTIVATED
@@ -35,50 +39,68 @@ class AlarmClock:
self.wake_time = wake_time
return self.wake_time
- def start_alarm(self, check_frequency: int = 1) -> bool:
- print(f"STARTING ALARM")
- self.current_state = AlarmState.STARTED
- now = datetime.now().time()
- if now >= self.wake_time:
- return False
- sleep_time_amount = check_frequency if check_frequency > 1 else 1
- while now < self.wake_time:
- # Sleep for some time
- sleep(sleep_time_amount)
- now = datetime.now().time()
- self.sound_alarm()
- return True
-
- def sound_alarm(self, sound: Optional = None, volume: int = DEFAULT_VOLUME):
- print(f"SOUNDING ALARM")
+ def start_alarm(self, check_frequency: int = 1):
+ print("STARTING ALARM")
+ self.current_state = AlarmState.RUNNING
+ # now = datetime.now().time()
+ # if now >= self.wake_time:
+ # return False
+ # sleep_time_amount = max(check_frequency, 1)
+ # while now < self.wake_time:
+ # # Sleep for some time
+ # sleep(sleep_time_amount)
+ # now = datetime.now().time()
+ # self.sound_alarm()
+ # return True
+
+ def sound_alarm(self):
+ print("SOUNDING ALARM")
self.current_state = AlarmState.PLAYING
- if sound is not None:
- self.player.set_media(sound)
- self.player.audio_set_volume(volume)
- self.player.play()
+ # if sound is not None:
+ # self.player.set_media(sound)
+ # self.player.audio_set_volume(volume)
+ # self.player.play()
- def snooze_alarm(self, snooze_time: int = 60):
- print(f"SNOOZING ALARM")
+ def snooze_alarm(self, snooze_time: int = SNOOZE_SEC):
+ print("SNOOZING ALARM")
self.current_state = AlarmState.SNOOZED
- self.player.pause()
+ # self.player.pause()
# Maybe make this async later
sleep(snooze_time)
- self.player.play()
+ # self.player.play()
if self.current_state is AlarmState.SNOOZED:
self.sound_alarm()
def stop_alarm(self, deactivate: bool = True):
- print(f"STOPPING ALARM")
+ print("STOPPING ALARM")
if self.current_state is AlarmState.PLAYING:
- self.player.stop()
+ print("STOPPED PLAYING ALARM")
+ # self.player.stop()
self.current_state = AlarmState.DEACTIVATED if deactivate else AlarmState.SET
+ def alarm_check_reached(self, current_time = datetime.now().time()) -> bool:
+ result = current_time >= self.wake_time if self.current_state is AlarmState.RUNNING else False
+ print(f"{result = }")
+ return result
+
+
+def simple_alarm_mode(alarm_time):
+ alarm_clock = BaseAlarmClock(alarm_time)
+ alarm_clock.start_alarm()
+ while not alarm_clock.alarm_check_reached():
+ print(f"ALARM SLEEPING @: {datetime.now().time()}")
+ sleep(1)
+ alarm_clock.sound_alarm()
+ while alarm_clock.current_state is not AlarmState.DEACTIVATED:
+ with keyboard.Events() as events:
+ for event in events:
+ if event.key == ALARM_OFF_KEY:
+ alarm_clock.stop_alarm()
+ break
+ elif event.key == SNOOZE_KEY:
+ alarm_clock.snooze_alarm()
+ break
-def simple_alarm_mode():
- alarm_clock = AlarmClock(math, ALARM_TIME)
-
-
-ALARM_TIME = time(7, 0, 0)
if __name__ == '__main__':
- simple_alarm_mode()
\ No newline at end of file
+ simple_alarm_mode(ALARM_TIME)
diff --git a/smart_alarm_clock.py b/smart_alarm_clock.py
new file mode 100644
index 0000000..e69de29
diff --git a/tf_model.ipynb b/tf_model.ipynb
index 0043c5f..1256f64 100644
--- a/tf_model.ipynb
+++ b/tf_model.ipynb
@@ -25,7 +25,8 @@
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"import pandas as pd\n",
- "from attention import Attention"
+ "from attention import Attention\n",
+ "import json"
]
},
{
@@ -53,7 +54,8 @@
"source": [
"# CONSTANTS\n",
"RAW_SLEEP_DATA_PATH = \".data/raw_bed_sleep-state.csv\"\n",
- "CLEANED_SLEEP_DATA_PATH = \".data/clean_bed_sleep-state.csv\""
+ "CLEANED_SLEEP_DATA_PATH = \".data/clean_bed_sleep-state.csv\"\n",
+ "SLEEP_DATA_PATH = \".data/sleep_data_simple.csv\""
]
},
{
@@ -692,14 +694,14 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
- "TEST_SIZE = 122\n",
- "VALIDATION_SIZE = 183\n",
+ "TEST_SIZE = 365*3//5\n",
+ "VALIDATION_SIZE = 365*4//5\n",
"\n",
- "BATCH_SIZE = 32\n",
+ "BATCH_SIZE = 64\n",
"INPUT_TIME_STEP = 10 # in minutes\n",
"INPUT_FEATURES_SIZE = 7\n",
"MAX_EPOCHS = 20"
@@ -707,16 +709,16 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
- "sleep_data = pd.read_csv(\".data/sleep_data_simple.csv\")"
+ "sleep_data = pd.read_csv(SLEEP_DATA_PATH)"
]
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -740,7 +742,6 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>Unnamed: 0</th>\n",
" <th>sleep_id</th>\n",
" <th>minutes_since_begin</th>\n",
" <th>stage_start_hour</th>\n",
@@ -756,7 +757,6 @@
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>0</td>\n",
" <td>8</td>\n",
" <td>18</td>\n",
" <td>1.0</td>\n",
@@ -766,7 +766,6 @@
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
@@ -778,7 +777,6 @@
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
@@ -790,7 +788,6 @@
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
@@ -802,7 +799,6 @@
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>4</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
@@ -822,11 +818,9 @@
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
- " <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>551037</th>\n",
- " <td>551037</td>\n",
" <td>1132</td>\n",
" <td>426</td>\n",
" <td>13</td>\n",
@@ -838,7 +832,6 @@
" </tr>\n",
" <tr>\n",
" <th>551038</th>\n",
- " <td>551038</td>\n",
" <td>1132</td>\n",
" <td>427</td>\n",
" <td>13</td>\n",
@@ -850,7 +843,6 @@
" </tr>\n",
" <tr>\n",
" <th>551039</th>\n",
- " <td>551039</td>\n",
" <td>1132</td>\n",
" <td>428</td>\n",
" <td>13</td>\n",
@@ -862,7 +854,6 @@
" </tr>\n",
" <tr>\n",
" <th>551040</th>\n",
- " <td>551040</td>\n",
" <td>1132</td>\n",
" <td>429</td>\n",
" <td>13</td>\n",
@@ -874,7 +865,6 @@
" </tr>\n",
" <tr>\n",
" <th>551041</th>\n",
- " <td>551041</td>\n",
" <td>1132</td>\n",
" <td>430</td>\n",
" <td>13</td>\n",
@@ -886,53 +876,53 @@
" </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>551042 rows × 9 columns</p>\n",
+ "<p>551042 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
- " Unnamed: 0 sleep_id minutes_since_begin stage_start_hour \\\n",
- "0 0 0 0 8 \n",
- "1 1 0 1 8 \n",
- "2 2 0 2 8 \n",
- "3 3 0 3 8 \n",
- "4 4 0 4 8 \n",
- "... ... ... ... ... \n",
- "551037 551037 1132 426 13 \n",
- "551038 551038 1132 427 13 \n",
- "551039 551039 1132 428 13 \n",
- "551040 551040 1132 429 13 \n",
- "551041 551041 1132 430 13 \n",
+ " sleep_id minutes_since_begin stage_start_hour stage_start_minute \\\n",
+ "0 0 0 8 18 \n",
+ "1 0 1 8 19 \n",
+ "2 0 2 8 20 \n",
+ "3 0 3 8 21 \n",
+ "4 0 4 8 22 \n",
+ "... ... ... ... ... \n",
+ "551037 1132 426 13 17 \n",
+ "551038 1132 427 13 18 \n",
+ "551039 1132 428 13 19 \n",
+ "551040 1132 429 13 20 \n",
+ "551041 1132 430 13 21 \n",
"\n",
- " stage_start_minute awake_probability rem_probability \\\n",
- "0 18 1.0 0.0 \n",
- "1 19 1.0 0.0 \n",
- "2 20 1.0 0.0 \n",
- "3 21 1.0 0.0 \n",
- "4 22 1.0 0.0 \n",
- "... ... ... ... \n",
- "551037 17 0.0 0.0 \n",
- "551038 18 0.0 0.0 \n",
- "551039 19 0.0 0.0 \n",
- "551040 20 0.0 0.0 \n",
- "551041 21 0.0 0.0 \n",
+ " awake_probability rem_probability light_probability \\\n",
+ "0 1.0 0.0 0.0 \n",
+ "1 1.0 0.0 0.0 \n",
+ "2 1.0 0.0 0.0 \n",
+ "3 1.0 0.0 0.0 \n",
+ "4 1.0 0.0 0.0 \n",
+ "... ... ... ... \n",
+ "551037 0.0 0.0 1.0 \n",
+ "551038 0.0 0.0 1.0 \n",
+ "551039 0.0 0.0 1.0 \n",
+ "551040 0.0 0.0 1.0 \n",
+ "551041 0.0 0.0 1.0 \n",
"\n",
- " light_probability deep_probability \n",
- "0 0.0 0.0 \n",
- "1 0.0 0.0 \n",
- "2 0.0 0.0 \n",
- "3 0.0 0.0 \n",
- "4 0.0 0.0 \n",
- "... ... ... \n",
- "551037 1.0 0.0 \n",
- "551038 1.0 0.0 \n",
- "551039 1.0 0.0 \n",
- "551040 1.0 0.0 \n",
- "551041 1.0 0.0 \n",
+ " deep_probability \n",
+ "0 0.0 \n",
+ "1 0.0 \n",
+ "2 0.0 \n",
+ "3 0.0 \n",
+ "4 0.0 \n",
+ "... ... \n",
+ "551037 0.0 \n",
+ "551038 0.0 \n",
+ "551039 0.0 \n",
+ "551040 0.0 \n",
+ "551041 0.0 \n",
"\n",
- "[551042 rows x 9 columns]"
+ "[551042 rows x 8 columns]"
]
},
- "execution_count": 35,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -950,7 +940,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -961,7 +951,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -969,7 +959,7 @@
"class WindowGenerator():\n",
" def __init__(self, data, index: str = \"sleep_id\", input_width: int = INPUT_TIME_STEP, validation_size: int = VALIDATION_SIZE, test_size: int = TEST_SIZE, input_feature_slice: slice = slice(1,100), label_feature_slice: slice = slice(-4,100), generate_data_now: bool = True):\n",
" # Partition data\n",
- " self.training, self.testing = training_test_split_by_unique_index(sleep_data, index, test_size)\n",
+ " self.training, self.testing = training_test_split_by_unique_index(data, index, test_size)\n",
" self.training, self.validation = training_test_split_by_unique_index(self.training, index, validation_size)\n",
"\n",
" # Window paramters\n",
@@ -989,7 +979,7 @@
" self.input_feature_slice = input_feature_slice\n",
" self.label_feature_slice = label_feature_slice\n",
"\n",
- " self.sample_ds = self.make_dataset(sleep_data[sleep_data[index] == 0])\n",
+ " self.sample_ds = self.make_dataset(data[data[index] == 0])\n",
"\n",
" if generate_data_now:\n",
" self.training_ds = self.make_dataset(self.training, index)\n",
@@ -1025,139 +1015,286 @@
" ds_all = ds if ds_all is None else ds_all.concatenate(ds)\n",
" ds_all = ds_all.map(self.split_window)\n",
"\n",
- " return ds_all\n",
- "\n",
- " # def generate_all_datasets(self):\n",
- " # self._training_ds = self.make_dataset(self.training)\n",
- " # self._validation_ds = self.make_dataset(self.validation)\n",
- " # self._testing_ds = self.make_dataset(self.testing)\n",
+ " return ds_all"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### General Model Helper"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Adapted from https://www.tensorflow.org/tutorials/structured_data/time_series#linear_model\n",
+ "def compile_and_fit(model, window: WindowGenerator, loss = tf.losses.CategoricalCrossentropy(from_logits=True), optimizer = tf.optimizers.Adam(), metrics = None, early_stop: bool = True, patience:int = 5, baseline = None, epochs: int = MAX_EPOCHS):\n",
+ " if metrics is None:\n",
+ " metrics = [tf.keras.metrics.CategoricalCrossentropy(from_logits=True), tf.keras.metrics.CategoricalAccuracy(), tf.keras.metrics.CategoricalHinge()]\n",
"\n",
- " # def training_dataset(self):\n",
- " # if self._training_ds is None:\n",
- " # self._training_ds = self.make_dataset(self.training)\n",
- " # return self._training_ds\n",
+ " callbacks = []\n",
+ " if early_stop:\n",
+ " early_stopping = tf.keras.callbacks.EarlyStopping(\n",
+ " monitor='val_loss',\n",
+ " patience=patience,\n",
+ " baseline = baseline,\n",
+ " mode='min'\n",
+ " )\n",
+ " callbacks.append(early_stopping)\n",
"\n",
- " # def validation_dataset(self):\n",
- " # if self._validation_ds is None:\n",
- " # self._validation_ds = self.make_dataset(self.validation)\n",
- " # return self._validation_ds\n",
+ " model.compile(\n",
+ " loss=loss,\n",
+ " optimizer=optimizer,\n",
+ " metrics=metrics,\n",
+ " )\n",
"\n",
- " # def test_dataset(self):\n",
- " # if self._testing_ds is None:\n",
- " # self._testing_ds = self.make_dataset(self.testing)\n",
- " # return self._testing_ds"
+ " return model.fit(window.training_ds, epochs=epochs, validation_data=window.validation_ds, callbacks=callbacks)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Data Prep\n",
- "\n",
- "All inputs follow: (batch_size, timesteps, input_dim)"
+ "### Experimenting"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 23,
"metadata": {},
- "outputs": [
- {
- "ename": "KeyError",
- "evalue": "'sleep_id'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "File \u001b[0;32m~/Documents/School Folder/CS 437/Lab/Final Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3619'>3620</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3620'>3621</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3621'>3622</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
- "File \u001b[0;32mpandas/_libs/index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
- "File \u001b[0;32mpandas/_libs/index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
- "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
- "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
- "\u001b[0;31mKeyError\u001b[0m: 'sleep_id'",
- "\nThe above exception was the direct cause of the following exception:\n",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m/Users/nowadmin/Documents/School Folder/CS 437/Lab/Final Project/tf_model.ipynb Cell 33'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000072?line=0'>1</a>\u001b[0m wg \u001b[39m=\u001b[39m WindowGenerator(sleep_data)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000072?line=1'>2</a>\u001b[0m wg\n",
- "\u001b[1;32m/Users/nowadmin/Documents/School Folder/CS 437/Lab/Final Project/tf_model.ipynb Cell 31'\u001b[0m in \u001b[0;36mWindowGenerator.__init__\u001b[0;34m(self, data, index, input_width, validation_size, test_size, input_feature_slice, label_feature_slice, generate_data_now)\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000065?line=2'>3</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, data, index: \u001b[39mstr\u001b[39m \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39msleep_id\u001b[39m\u001b[39m\"\u001b[39m, input_width: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m INPUT_TIME_STEP, validation_size: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m VALIDATION_SIZE, test_size: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m TEST_SIZE, input_feature_slice: \u001b[39mslice\u001b[39m \u001b[39m=\u001b[39m \u001b[39mslice\u001b[39m(\u001b[39m1\u001b[39m,\u001b[39m100\u001b[39m), label_feature_slice: \u001b[39mslice\u001b[39m \u001b[39m=\u001b[39m \u001b[39mslice\u001b[39m(\u001b[39m-\u001b[39m\u001b[39m4\u001b[39m,\u001b[39m100\u001b[39m), generate_data_now: \u001b[39mbool\u001b[39m \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m):\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000065?line=3'>4</a>\u001b[0m \u001b[39m# Partition data\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000065?line=4'>5</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtesting \u001b[39m=\u001b[39m training_test_split_by_unique_index(sleep_data, index, test_size)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000065?line=5'>6</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvalidation \u001b[39m=\u001b[39m training_test_split_by_unique_index(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining, index, validation_size)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000065?line=7'>8</a>\u001b[0m \u001b[39m# Window paramters\u001b[39;00m\n",
- "\u001b[1;32m/Users/nowadmin/Documents/School Folder/CS 437/Lab/Final Project/tf_model.ipynb Cell 30'\u001b[0m in \u001b[0;36mtraining_test_split_by_unique_index\u001b[0;34m(data, index, test_size)\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000066?line=0'>1</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mtraining_test_split_by_unique_index\u001b[39m(data, index: \u001b[39mstr\u001b[39m, test_size: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m \u001b[39m10\u001b[39m):\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000066?line=1'>2</a>\u001b[0m test_ids \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mchoice(data[index]\u001b[39m.\u001b[39munique(), size \u001b[39m=\u001b[39m test_size, replace\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000066?line=2'>3</a>\u001b[0m \u001b[39mreturn\u001b[39;00m data[\u001b[39m~\u001b[39mdata[index]\u001b[39m.\u001b[39misin(test_ids)], data[data[index]\u001b[39m.\u001b[39misin(test_ids)]\n",
- "File \u001b[0;32m~/Documents/School Folder/CS 437/Lab/Final Project/venv/lib/python3.10/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/frame.py?line=3502'>3503</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/frame.py?line=3503'>3504</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/frame.py?line=3504'>3505</a>\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/frame.py?line=3505'>3506</a>\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/frame.py?line=3506'>3507</a>\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n",
- "File \u001b[0;32m~/Documents/School Folder/CS 437/Lab/Final Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3620'>3621</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3621'>3622</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3622'>3623</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3623'>3624</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3624'>3625</a>\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3625'>3626</a>\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3626'>3627</a>\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3627'>3628</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
- "\u001b[0;31mKeyError\u001b[0m: 'sleep_id'"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "wg = WindowGenerator(sleep_data)\n",
- "wg"
+ "# USE SUBSET OF DATA FOR EXPERIMENTING\n",
+ "sleep_data_sub = sleep_data[sleep_data.sleep_id < 2000]"
]
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
- "sample = wg.sample_ds.take(1)"
+ "wg_sub = WindowGenerator(sleep_data_sub,validation_size=365, test_size=182)"
]
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 25,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_2\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " dense_4 (Dense) (None, 10, 16) 128 \n",
+ " \n",
+ " flatten_2 (Flatten) (None, 160) 0 \n",
+ " \n",
+ " dense_5 (Dense) (None, 4) 644 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 772\n",
+ "Trainable params: 772\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n",
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "BASELINE_UNITS = 16\n",
+ "baseline_model = keras.Sequential(\n",
+ " [\n",
+ " layers.Input(shape=(INPUT_TIME_STEP, INPUT_FEATURES_SIZE)),\n",
+ " layers.Dense(BASELINE_UNITS),\n",
+ " # layers.Dense(BASELINE_UNITS),\n",
+ " # layers.Dense(BASELINE_UNITS),\n",
+ " layers.Flatten(),\n",
+ " layers.Dense(SLEEP_STAGES),\n",
+ " ]\n",
+ ")\n",
+ "baseline_model.build()\n",
+ "print(baseline_model.summary())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n",
+ "8955/8955 [==============================] - 60s 7ms/step - loss: 2.7762 - categorical_crossentropy: 2.7745 - categorical_accuracy: 0.7612 - categorical_hinge: 15.1798 - val_loss: 0.2711 - val_categorical_crossentropy: 0.2710 - val_categorical_accuracy: 0.9546 - val_categorical_hinge: 5.1404\n",
+ "Epoch 2/20\n",
+ "8955/8955 [==============================] - 60s 7ms/step - loss: 0.3110 - categorical_crossentropy: 0.3111 - categorical_accuracy: 0.9418 - categorical_hinge: 1.5468 - val_loss: 0.2106 - val_categorical_crossentropy: 0.2105 - val_categorical_accuracy: 0.9583 - val_categorical_hinge: 0.3215\n",
+ "Epoch 3/20\n",
+ "8955/8955 [==============================] - 60s 7ms/step - loss: 0.2253 - categorical_crossentropy: 0.2254 - categorical_accuracy: 0.9551 - categorical_hinge: 0.2726 - val_loss: 0.2068 - val_categorical_crossentropy: 0.2067 - val_categorical_accuracy: 0.9582 - val_categorical_hinge: 0.3656\n",
+ "Epoch 4/20\n",
+ "8955/8955 [==============================] - 63s 7ms/step - loss: 0.2059 - categorical_crossentropy: 0.2059 - categorical_accuracy: 0.9582 - categorical_hinge: 0.2735 - val_loss: 0.1957 - val_categorical_crossentropy: 0.1957 - val_categorical_accuracy: 0.9591 - val_categorical_hinge: 0.3622\n",
+ "Epoch 5/20\n",
+ "8955/8955 [==============================] - 82s 9ms/step - loss: 0.2016 - categorical_crossentropy: 0.2016 - categorical_accuracy: 0.9587 - categorical_hinge: 0.2638 - val_loss: 0.1970 - val_categorical_crossentropy: 0.1970 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.2868\n",
+ "Epoch 6/20\n",
+ "8955/8955 [==============================] - 72s 8ms/step - loss: 0.1995 - categorical_crossentropy: 0.1995 - categorical_accuracy: 0.9585 - categorical_hinge: 0.2506 - val_loss: 0.1973 - val_categorical_crossentropy: 0.1973 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.3453\n",
+ "Epoch 7/20\n",
+ "8955/8955 [==============================] - 75s 8ms/step - loss: 0.1991 - categorical_crossentropy: 0.1992 - categorical_accuracy: 0.9591 - categorical_hinge: 0.2518 - val_loss: 0.1969 - val_categorical_crossentropy: 0.1968 - val_categorical_accuracy: 0.9591 - val_categorical_hinge: 0.2474\n",
+ "Epoch 8/20\n",
+ "8955/8955 [==============================] - 75s 8ms/step - loss: 0.1983 - categorical_crossentropy: 0.1983 - categorical_accuracy: 0.9587 - categorical_hinge: 0.2560 - val_loss: 0.1957 - val_categorical_crossentropy: 0.1956 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.2912\n",
+ "Epoch 9/20\n",
+ "8955/8955 [==============================] - 76s 8ms/step - loss: 0.1965 - categorical_crossentropy: 0.1965 - categorical_accuracy: 0.9595 - categorical_hinge: 0.2328 - val_loss: 0.1957 - val_categorical_crossentropy: 0.1957 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.3268\n",
+ "Epoch 10/20\n",
+ "8955/8955 [==============================] - 77s 9ms/step - loss: 0.1961 - categorical_crossentropy: 0.1961 - categorical_accuracy: 0.9598 - categorical_hinge: 0.2375 - val_loss: 0.1954 - val_categorical_crossentropy: 0.1954 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.2518\n",
+ "Epoch 11/20\n",
+ "8955/8955 [==============================] - 72s 8ms/step - loss: 0.1980 - categorical_crossentropy: 0.1981 - categorical_accuracy: 0.9584 - categorical_hinge: 0.2387 - val_loss: 0.1948 - val_categorical_crossentropy: 0.1948 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.2795\n",
+ "Epoch 12/20\n",
+ "8955/8955 [==============================] - 69s 8ms/step - loss: 0.1959 - categorical_crossentropy: 0.1959 - categorical_accuracy: 0.9594 - categorical_hinge: 0.2465 - val_loss: 0.1950 - val_categorical_crossentropy: 0.1950 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.2824\n",
+ "Epoch 13/20\n",
+ "8955/8955 [==============================] - 65s 7ms/step - loss: 0.1960 - categorical_crossentropy: 0.1960 - categorical_accuracy: 0.9596 - categorical_hinge: 0.2413 - val_loss: 0.1955 - val_categorical_crossentropy: 0.1954 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.3859\n",
+ "Epoch 14/20\n",
+ "8955/8955 [==============================] - 68s 8ms/step - loss: 0.1959 - categorical_crossentropy: 0.1959 - categorical_accuracy: 0.9591 - categorical_hinge: 0.2280 - val_loss: 0.1941 - val_categorical_crossentropy: 0.1941 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.3046\n",
+ "Epoch 15/20\n",
+ "8955/8955 [==============================] - 77s 9ms/step - loss: 0.2031 - categorical_crossentropy: 0.2031 - categorical_accuracy: 0.9580 - categorical_hinge: 0.2336 - val_loss: 0.1949 - val_categorical_crossentropy: 0.1948 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.1873\n",
+ "Epoch 16/20\n",
+ "8955/8955 [==============================] - 68s 8ms/step - loss: 0.1966 - categorical_crossentropy: 0.1966 - categorical_accuracy: 0.9589 - categorical_hinge: 0.2361 - val_loss: 0.1938 - val_categorical_crossentropy: 0.1938 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.2056\n",
+ "Epoch 17/20\n",
+ "8955/8955 [==============================] - 67s 7ms/step - loss: 0.1924 - categorical_crossentropy: 0.1924 - categorical_accuracy: 0.9599 - categorical_hinge: 0.2510 - val_loss: 0.1934 - val_categorical_crossentropy: 0.1934 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.3111\n",
+ "Epoch 18/20\n",
+ "8955/8955 [==============================] - 67s 7ms/step - loss: 0.2079 - categorical_crossentropy: 0.2079 - categorical_accuracy: 0.9569 - categorical_hinge: 0.3273 - val_loss: 0.1930 - val_categorical_crossentropy: 0.1929 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.2956\n",
+ "Epoch 19/20\n",
+ "8955/8955 [==============================] - 67s 7ms/step - loss: 0.1923 - categorical_crossentropy: 0.1923 - categorical_accuracy: 0.9599 - categorical_hinge: 0.3248 - val_loss: 0.1924 - val_categorical_crossentropy: 0.1924 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.6417\n",
+ "Epoch 20/20\n",
+ "8955/8955 [==============================] - 68s 8ms/step - loss: 0.1950 - categorical_crossentropy: 0.1950 - categorical_accuracy: 0.9595 - categorical_hinge: 0.3199 - val_loss: 0.1935 - val_categorical_crossentropy: 0.1935 - val_categorical_accuracy: 0.9592 - val_categorical_hinge: 0.5098\n"
+ ]
+ }
+ ],
"source": [
- "sample_array = list(sample.as_numpy_iterator())"
+ "baseline_history = compile_and_fit(baseline_model, wg_sub)"
]
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "((10, 7), (4,))"
+ "dict_keys(['loss', 'categorical_crossentropy', 'categorical_accuracy', 'categorical_hinge', 'val_loss', 'val_categorical_crossentropy', 'val_categorical_accuracy', 'val_categorical_hinge'])"
]
},
- "execution_count": 63,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "INDEX_TIMESTEP = 18\n",
- "sample_array[0][0][INDEX_TIMESTEP], sample_array[0][1][INDEX_TIMESTEP]"
+ "baseline_history.history.keys()"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": 32,
"metadata": {},
+ "outputs": [],
"source": [
- "### General Model Helper"
+ "save_history(baseline_history.history, \".history/baseline.csv\")"
]
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
- "# Adapted from https://www.tensorflow.org/tutorials/structured_data/time_series#linear_model\n",
- "def compile_and_fit(model, window: WindowGenerator, loss = tf.losses.MeanSquaredError(), optimizer = tf.optimizers.Adam(), metrics = tf.metrics.MeanAbsoluteError(), patience:int = 2, epochs: int = MAX_EPOCHS):\n",
- " early_stopping = tf.keras.callbacks.EarlyStopping(\n",
- " monitor='val_loss',\n",
- " patience=patience,\n",
- " mode='min'\n",
- " )\n",
- "\n",
- " model.compile(\n",
- " loss=loss,\n",
- " optimizer=optimizer,\n",
- " metrics=metrics,\n",
- " )\n",
+ "def save_history(history, file_name: str = \"history.csv\"):\n",
+ " pd.DataFrame.from_dict(history).to_csv(file_name, index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-05-11 15:02:03.185843: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: .model/baseline/assets\n"
+ ]
+ }
+ ],
+ "source": [
+ "baseline_model.save(\".model/baseline\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data Prep\n",
"\n",
- " return model.fit(window.training_ds, epochs=epochs, validation_data=window.validation_ds, callbacks=[early_stopping])"
+ "All inputs follow: (batch_size, timesteps, input_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "WindowGenerator:\n",
+ "\tTotal window size: 11\n",
+ "\tInput indices: [0 1 2 3 4 5 6 7 8 9]\n",
+ "\tLabel indices: [10]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "wg = WindowGenerator(sleep_data)\n",
+ "wg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "9647"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(wg.training_ds)"
]
},
{
@@ -1169,41 +1306,20 @@
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"# Hyper-parameters\n",
- "LSTM_UNITS = 16\n",
+ "LSTM_UNITS = 8\n",
"LSTM_LEARNING_RATE = 0.0001"
]
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 37,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential_7\"\n",
- "_________________________________________________________________\n",
- " Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- " lstm_8 (LSTM) (None, 16) 1536 \n",
- " \n",
- " dense_7 (Dense) (None, 4) 68 \n",
- " \n",
- "=================================================================\n",
- "Total params: 1,604\n",
- "Trainable params: 1,604\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n",
- "None\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Model Definition\n",
"lstm_model = keras.Sequential()\n",
@@ -1218,47 +1334,31 @@
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"# Model Training\n",
- "lstm_loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
- "lstm_optm = keras.optimizers.Adam(learning_rate=LSTM_LEARNING_RATE)\n",
- "lstm_metrics = [tf.keras.metrics.SparseCategoricalCrossentropy(from_logits=True), tf.keras.metrics.Accuracy()]"
+ "lstm_loss = tf.losses.CategoricalCrossentropy(from_logits=True)\n",
+ "lstm_optm = tf.optimizers.Adam(learning_rate=LSTM_LEARNING_RATE)\n",
+ "lstm_metrics = [tf.keras.metrics.CategoricalCrossentropy(from_logits=True), tf.keras.metrics.BinaryCrossentropy(from_logits=True),tf.keras.metrics.Accuracy()]"
]
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Epoch 1/20\n"
- ]
- },
- {
- "ename": "",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31mCanceled future for execute_request message before replies were done"
- ]
- },
- {
- "ename": "",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+ "9647/9647 [==============================] - 88s 9ms/step - loss: 0.6389 - categorical_crossentropy: 0.6388 - categorical_accuracy: 0.7669 - categorical_hinge: 0.5620 - val_loss: 0.2218 - val_categorical_crossentropy: 0.2217 - val_categorical_accuracy: 0.9551 - val_categorical_hinge: 0.2163\n"
]
}
],
"source": [
- "lstm_history = compile_and_fit(model=lstm_model, window=wg, loss= lstm_loss, optimizer= lstm_optm, metrics=lstm_metrics)"
+ "lstm_history = compile_and_fit(model=lstm_model, window=wg, epochs=1)"
]
},
{
@@ -1270,7 +1370,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
@@ -1280,24 +1380,55 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 45,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_8\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " gru_7 (GRU) (None, 16) 1200 \n",
+ " \n",
+ " dense_14 (Dense) (None, 4) 68 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 1,268\n",
+ "Trainable params: 1,268\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n",
+ "None\n"
+ ]
+ }
+ ],
"source": [
- "gru_model = keras.Sequential([\n",
- " layers.GRU(GRU_UNITS),\n",
- " layers.Dense(SLEEP_STAGES)\n",
- "])\n",
- "gru_model.add(layers.Embedding(input_dim=1000, output_dim=64))\n",
+ "gru_model = keras.Sequential()\n",
+ "gru_model.add(layers.Input(shape=(INPUT_TIME_STEP, INPUT_FEATURES_SIZE)))\n",
+ "gru_model.add(layers.GRU(GRU_UNITS))\n",
+ "gru_model.add(layers.Dense(SLEEP_STAGES))\n",
+ "gru_model.build()\n",
"print(gru_model.summary())"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 46,
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "9647/9647 [==============================] - 97s 10ms/step - loss: 0.4700 - categorical_crossentropy: 0.4700 - categorical_accuracy: 0.8416 - categorical_hinge: 0.4278 - val_loss: 0.2088 - val_categorical_crossentropy: 0.2087 - val_categorical_accuracy: 0.9595 - val_categorical_hinge: 0.1954\n"
+ ]
+ }
+ ],
+ "source": [
+ "gru_history = compile_and_fit(model=gru_model, window=wg, epochs=1)"
+ ]
},
{
"cell_type": "markdown",
@@ -1308,7 +1439,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
@@ -1317,23 +1448,55 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 49,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_10\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " attention_1 (Attention) (None, 16) 273 \n",
+ " \n",
+ " dense_16 (Dense) (None, 4) 68 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 341\n",
+ "Trainable params: 341\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n",
+ "None\n"
+ ]
+ }
+ ],
"source": [
- "am_model = keras.Sequential([\n",
- " Attention(ATTENTION_UNITS)\n",
- " layers.Dense(SLEEP_STAGES)\n",
- "])\n",
+ "am_model = keras.Sequential()\n",
+ "am_model.add(layers.Input(shape=(INPUT_TIME_STEP, INPUT_FEATURES_SIZE)))\n",
+ "am_model.add(Attention(ATTENTION_UNITS))\n",
+ "am_model.add(layers.Dense(SLEEP_STAGES))\n",
+ "am_model.build()\n",
"print(am_model.summary())"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 50,
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "9647/9647 [==============================] - 52s 5ms/step - loss: 0.6620 - categorical_crossentropy: 0.6619 - categorical_accuracy: 0.7268 - categorical_hinge: 0.7192 - val_loss: 0.2141 - val_categorical_crossentropy: 0.2140 - val_categorical_accuracy: 0.9589 - val_categorical_hinge: 0.1990\n"
+ ]
+ }
+ ],
+ "source": [
+ "am_history = compile_and_fit(model=am_model, window=wg, epochs=1)"
+ ]
},
{
"cell_type": "markdown",
--
GitLab