diff --git a/.gitignore b/.gitignore
index 2269427c1edc001edae94bdf7e9fce0e53fad2d8..988c81eb83b80be97164153692a1b51b5c4c55a1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
 *.pdf
 .data/
-*.pyc
\ No newline at end of file
+*.pyc
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new file mode 100755
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new file mode 100755
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new file mode 100644
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new file mode 100755
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new file mode 100755
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diff --git a/.model/attention/keras_metadata.pb b/.model/attention/keras_metadata.pb
new file mode 100755
index 0000000000000000000000000000000000000000..a85a548eb098711de803bb25c8e4aac7f2e3d7e9
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+µ
root.layer_with_weights-0"_tf_keras_layer*þ{"name": "attention", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Attention", "config": {"name": "attention", "trainable": true, "dtype": "float32", "units": 16}, "shared_object_id": 1, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 7]}}2

+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": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 4, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 16}}, "shared_object_id": 11}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16]}}2

+ø-root.layer_with_weights-0.attention_score_vec"_tf_keras_layer*­{"name": "attention_score_vec", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "attention_score_vec", "trainable": true, "dtype": "float32", "units": 7, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 12}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 13}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 14, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 7}}, "shared_object_id": 15}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 7]}}2

+·
root.layer_with_weights-0.h_t"_tf_keras_layer*ü{"name": "last_hidden_state", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Lambda", "config": {"name": "last_hidden_state", "trainable": true, "dtype": "float32", "function": {"class_name": "__tuple__", "items": ["4wEAAAAAAAAAAAAAAAEAAAAFAAAAUwAAAHMWAAAAfABkAGQAhQJkAWQAZACFAmYDGQBTACkCTun/\n////qQApAdoBeHICAAAAcgIAAAD6MS9ob21lL250b3AvUHljaGFybVByb2plY3RzL2ZpbmFsX2xh\nYi9hdHRlbnRpb24ucHnaCDxsYW1iZGE+GwAAAHMCAAAAFgA=\n", null, null]}, "function_type": "lambda", "module": "attention", "output_shape": {"class_name": "__tuple__", "items": [7]}, "output_shape_type": "raw", "output_shape_module": null, "arguments": {}}, "shared_object_id": 16}2

+™)root.layer_with_weights-0.attention_score"_tf_keras_layer*Ò{"name": "attention_score", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dot", "config": {"name": "attention_score", "trainable": true, "dtype": "float32", "axes": [1, 2], "normalize": false}, "shared_object_id": 17, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 7]}, {"class_name": "TensorShape", "items": [null, 10, 7]}]}2

+—*root.layer_with_weights-0.attention_weight"_tf_keras_layer*Ï{"name": "attention_weight", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "attention_weight", "trainable": true, "dtype": "float32", "activation": "softmax"}, "shared_object_id": 18}2

+—(root.layer_with_weights-0.context_vector"_tf_keras_layer*Ñ{"name": "context_vector", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dot", "config": {"name": "context_vector", "trainable": true, "dtype": "float32", "axes": [1, 1], "normalize": false}, "shared_object_id": 19, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 10, 7]}, {"class_name": "TensorShape", "items": [null, 10]}]}2

+ˆ*root.layer_with_weights-0.attention_output"_tf_keras_layer*À{"name": "attention_output", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Concatenate", "config": {"name": "attention_output", "trainable": true, "dtype": "float32", "axis": -1}, "shared_object_id": 20, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 7]}, {"class_name": "TensorShape", "items": [null, 7]}]}2

+ì*root.layer_with_weights-0.attention_vector"_tf_keras_layer*¤{"name": "attention_vector", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "attention_vector", "trainable": true, "dtype": "float32", "units": 16, "activation": "tanh", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 21}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 22}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 23, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 14}}, "shared_object_id": 24}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 14]}}2

+¹
droot.keras_api.metrics.0"_tf_keras_metric*‚
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 25}2

+žeroot.keras_api.metrics.1"_tf_keras_metric*ç
{"class_name": "CategoricalCrossentropy", "name": "categorical_crossentropy", "dtype": "float32", "config": {"name": "categorical_crossentropy", "dtype": "float32", "from_logits": true, "label_smoothing": 0}, "shared_object_id": 8}2

+ç
froot.keras_api.metrics.2"_tf_keras_metric*°
{"class_name": "CategoricalAccuracy", "name": "categorical_accuracy", "dtype": "float32", "config": {"name": "categorical_accuracy", "dtype": "float32"}, "shared_object_id": 9}2

+ß
groot.keras_api.metrics.3"_tf_keras_metric*¨
{"class_name": "CategoricalHinge", "name": "categorical_hinge", "dtype": "float32", "config": {"name": "categorical_hinge", "dtype": "float32"}, "shared_object_id": 10}2

\ No newline at end of file
diff --git a/.model/attention/saved_model.pb b/.model/attention/saved_model.pb
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new file mode 100755
index 0000000000000000000000000000000000000000..1a5c7b2d5f137e1a1170e292ea9a9f2f47e7e253
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diff --git a/.model/attention/variables/variables.index b/.model/attention/variables/variables.index
new file mode 100755
index 0000000000000000000000000000000000000000..a31867933f61483597b04caaeebd956bbc4d30f3
Binary files /dev/null and b/.model/attention/variables/variables.index differ
diff --git a/.model/attention_old/keras_metadata.pb b/.model/attention_old/keras_metadata.pb
new file mode 100755
index 0000000000000000000000000000000000000000..a85a548eb098711de803bb25c8e4aac7f2e3d7e9
--- /dev/null
+++ b/.model/attention_old/keras_metadata.pb
@@ -0,0 +1,15 @@
+
+‘root"_tf_keras_sequential*ì{"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": "Attention", "config": {"name": "attention", "trainable": true, "dtype": "float32", "units": 16}}, {"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}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "shared_object_id": 5, "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": "Attention", "config": {"name": "attention", "trainable": true, "dtype": "float32", "units": 16}, "shared_object_id": 1}, {"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": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 4}]}}, "training_config": {"loss": {"class_name": "CategoricalCrossentropy", "config": {"reduction": "auto", "name": "categorical_crossentropy", "from_logits": true, "label_smoothing": 0.0, "axis": -1}, "shared_object_id": 7}, "metrics": [[{"class_name": "CategoricalCrossentropy", "config": {"name": "categorical_crossentropy", "dtype": "float32", "from_logits": true, "label_smoothing": 0}, "shared_object_id": 8}, {"class_name": "CategoricalAccuracy", "config": {"name": "categorical_accuracy", "dtype": "float32"}, "shared_object_id": 9}, {"class_name": "CategoricalHinge", "config": {"name": "categorical_hinge", "dtype": "float32"}, "shared_object_id": 10}]], "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

+µ
root.layer_with_weights-0"_tf_keras_layer*þ{"name": "attention", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Attention", "config": {"name": "attention", "trainable": true, "dtype": "float32", "units": 16}, "shared_object_id": 1, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 7]}}2

+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": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 4, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 16}}, "shared_object_id": 11}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16]}}2

+ø-root.layer_with_weights-0.attention_score_vec"_tf_keras_layer*­{"name": "attention_score_vec", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "attention_score_vec", "trainable": true, "dtype": "float32", "units": 7, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 12}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 13}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 14, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 7}}, "shared_object_id": 15}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 7]}}2

+·
root.layer_with_weights-0.h_t"_tf_keras_layer*ü{"name": "last_hidden_state", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Lambda", "config": {"name": "last_hidden_state", "trainable": true, "dtype": "float32", "function": {"class_name": "__tuple__", "items": ["4wEAAAAAAAAAAAAAAAEAAAAFAAAAUwAAAHMWAAAAfABkAGQAhQJkAWQAZACFAmYDGQBTACkCTun/\n////qQApAdoBeHICAAAAcgIAAAD6MS9ob21lL250b3AvUHljaGFybVByb2plY3RzL2ZpbmFsX2xh\nYi9hdHRlbnRpb24ucHnaCDxsYW1iZGE+GwAAAHMCAAAAFgA=\n", null, null]}, "function_type": "lambda", "module": "attention", "output_shape": {"class_name": "__tuple__", "items": [7]}, "output_shape_type": "raw", "output_shape_module": null, "arguments": {}}, "shared_object_id": 16}2

+™)root.layer_with_weights-0.attention_score"_tf_keras_layer*Ò{"name": "attention_score", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dot", "config": {"name": "attention_score", "trainable": true, "dtype": "float32", "axes": [1, 2], "normalize": false}, "shared_object_id": 17, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 7]}, {"class_name": "TensorShape", "items": [null, 10, 7]}]}2

+—*root.layer_with_weights-0.attention_weight"_tf_keras_layer*Ï{"name": "attention_weight", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "attention_weight", "trainable": true, "dtype": "float32", "activation": "softmax"}, "shared_object_id": 18}2

+—(root.layer_with_weights-0.context_vector"_tf_keras_layer*Ñ{"name": "context_vector", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dot", "config": {"name": "context_vector", "trainable": true, "dtype": "float32", "axes": [1, 1], "normalize": false}, "shared_object_id": 19, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 10, 7]}, {"class_name": "TensorShape", "items": [null, 10]}]}2

+ˆ*root.layer_with_weights-0.attention_output"_tf_keras_layer*À{"name": "attention_output", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Concatenate", "config": {"name": "attention_output", "trainable": true, "dtype": "float32", "axis": -1}, "shared_object_id": 20, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 7]}, {"class_name": "TensorShape", "items": [null, 7]}]}2

+ì*root.layer_with_weights-0.attention_vector"_tf_keras_layer*¤{"name": "attention_vector", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "attention_vector", "trainable": true, "dtype": "float32", "units": 16, "activation": "tanh", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 21}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 22}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 23, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 14}}, "shared_object_id": 24}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 14]}}2

+¹
droot.keras_api.metrics.0"_tf_keras_metric*‚
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 25}2

+žeroot.keras_api.metrics.1"_tf_keras_metric*ç
{"class_name": "CategoricalCrossentropy", "name": "categorical_crossentropy", "dtype": "float32", "config": {"name": "categorical_crossentropy", "dtype": "float32", "from_logits": true, "label_smoothing": 0}, "shared_object_id": 8}2

+ç
froot.keras_api.metrics.2"_tf_keras_metric*°
{"class_name": "CategoricalAccuracy", "name": "categorical_accuracy", "dtype": "float32", "config": {"name": "categorical_accuracy", "dtype": "float32"}, "shared_object_id": 9}2

+ß
groot.keras_api.metrics.3"_tf_keras_metric*¨
{"class_name": "CategoricalHinge", "name": "categorical_hinge", "dtype": "float32", "config": {"name": "categorical_hinge", "dtype": "float32"}, "shared_object_id": 10}2

\ No newline at end of file
diff --git a/.model/attention_old/saved_model.pb b/.model/attention_old/saved_model.pb
new file mode 100755
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new file mode 100755
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diff --git a/.model/baseline/keras_metadata.pb b/.model/baseline/keras_metadata.pb
new file mode 100755
index 0000000000000000000000000000000000000000..f5bbc8ad4039df2c853242b4325c9b768c3998e8
--- /dev/null
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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

+Å
root.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
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diff --git a/.model/baseline/variables/variables.data-00000-of-00001 b/.model/baseline/variables/variables.data-00000-of-00001
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index 0000000000000000000000000000000000000000..4ab18dcf4968fd05683e51d344e93d6be682a6d2
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diff --git a/.model/baseline/variables/variables.index b/.model/baseline/variables/variables.index
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diff --git a/.model/baseline_old/keras_metadata.pb b/.model/baseline_old/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..f5bbc8ad4039df2c853242b4325c9b768c3998e8
--- /dev/null
+++ b/.model/baseline_old/keras_metadata.pb
@@ -0,0 +1,9 @@
+
+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

+Å
root.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

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new file mode 100644
index 0000000000000000000000000000000000000000..194bf61b1b64d0f8946548c1097f540c0b17d335
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diff --git a/.model/baseline_old/variables/variables.index b/.model/baseline_old/variables/variables.index
new file mode 100644
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Binary files /dev/null and b/.model/baseline_old/variables/variables.index differ
diff --git a/.model/gru/keras_metadata.pb b/.model/gru/keras_metadata.pb
new file mode 100755
index 0000000000000000000000000000000000000000..38cf9a65767f19a017a142115d02a1c5d7b27048
--- /dev/null
+++ b/.model/gru/keras_metadata.pb
@@ -0,0 +1,10 @@
+
+¦(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": "GRU", "config": {"name": "gru", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "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": 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+—
root.layer_with_weights-0"_tf_keras_rnn_layer*Ü
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+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
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diff --git a/.model/gru/variables/variables.index b/.model/gru/variables/variables.index
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diff --git a/.model/gru_old/keras_metadata.pb b/.model/gru_old/keras_metadata.pb
new file mode 100755
index 0000000000000000000000000000000000000000..38cf9a65767f19a017a142115d02a1c5d7b27048
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+—
root.layer_with_weights-0"_tf_keras_rnn_layer*Ü
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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

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diff --git a/.model/lstm/keras_metadata.pb b/.model/lstm/keras_metadata.pb
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"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}]}}, "training_config": {"loss": {"class_name": "CategoricalCrossentropy", "config": {"reduction": "auto", "name": "categorical_crossentropy", "from_logits": true, "label_smoothing": 0.0, "axis": -1}, "shared_object_id": 11}, "metrics": [[{"class_name": "CategoricalCrossentropy", "config": {"name": "categorical_crossentropy", "dtype": "float32", "from_logits": true, "label_smoothing": 0}, "shared_object_id": 12}, {"class_name": "CategoricalAccuracy", "config": {"name": "categorical_accuracy", "dtype": "float32"}, "shared_object_id": 13}, {"class_name": "CategoricalHinge", "config": {"name": "categorical_hinge", "dtype": "float32"}, "shared_object_id": 14}]], "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

+Ÿ
root.layer_with_weights-0"_tf_keras_rnn_layer*ä
+{"name": "lstm", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "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}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}, "shared_object_id": 5, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, null, 7]}, "ndim": 3, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 15}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 7]}}2

+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

+çroot.layer_with_weights-0.cell"_tf_keras_layer*«{"name": "lstm_cell", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "LSTMCell", "config": {"name": "lstm_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}, "unit_forget_bias": true, "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}, "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
Aroot.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/lstm/saved_model.pb b/.model/lstm/saved_model.pb
new file mode 100755
index 0000000000000000000000000000000000000000..89ec9e1fd65976f1177bdf98a2e0182073b03a8a
Binary files /dev/null and b/.model/lstm/saved_model.pb differ
diff --git a/.model/lstm/variables/variables.data-00000-of-00001 b/.model/lstm/variables/variables.data-00000-of-00001
new file mode 100755
index 0000000000000000000000000000000000000000..e251074298beb95730bbf2acaed354e54e769e78
Binary files /dev/null and b/.model/lstm/variables/variables.data-00000-of-00001 differ
diff --git a/.model/lstm/variables/variables.index b/.model/lstm/variables/variables.index
new file mode 100755
index 0000000000000000000000000000000000000000..a81c9896112a110c0ca3f6ef25a83759895e1cc5
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diff --git a/.model/lstm_old/keras_metadata.pb b/.model/lstm_old/keras_metadata.pb
new file mode 100755
index 0000000000000000000000000000000000000000..01e5f70bfa498b7a46d6c58a690bc945ddeb2b8f
--- /dev/null
+++ b/.model/lstm_old/keras_metadata.pb
@@ -0,0 +1,10 @@
+
+´(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": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "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}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"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}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "shared_object_id": 9, "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": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, 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{"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

+Ÿ
root.layer_with_weights-0"_tf_keras_rnn_layer*ä
+{"name": "lstm", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "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}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}, "shared_object_id": 5, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, null, 7]}, "ndim": 3, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 15}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 7]}}2

+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

+çroot.layer_with_weights-0.cell"_tf_keras_layer*«{"name": "lstm_cell", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "LSTMCell", "config": {"name": "lstm_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}, "unit_forget_bias": true, "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}, "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
Aroot.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/lstm_old/saved_model.pb b/.model/lstm_old/saved_model.pb
new file mode 100755
index 0000000000000000000000000000000000000000..4ebe7a8f70872e2e984bc6130d9990d5dff89769
Binary files /dev/null and b/.model/lstm_old/saved_model.pb differ
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
index 0000000000000000000000000000000000000000..ca6572b0159eb41a9b2b7b74de019cc8e4b575ba
Binary files /dev/null and b/.model/lstm_old/variables/variables.data-00000-of-00001 differ
diff --git a/.model/lstm_old/variables/variables.index b/.model/lstm_old/variables/variables.index
new file mode 100755
index 0000000000000000000000000000000000000000..d7e54b4c5f2b1a408dcecaf5c0a979dd043a5676
Binary files /dev/null and b/.model/lstm_old/variables/variables.index differ
diff --git a/attention_model.py b/attention_model.py
new file mode 100755
index 0000000000000000000000000000000000000000..f20fbfc0f7b9f2d7d3d00f8ca2606225cf33ee58
--- /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 0000000000000000000000000000000000000000..1bbfb63c4e7f58d0e6acec92f79de7f156b387f7
--- /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 0000000000000000000000000000000000000000..c3aca7b73666f4871341a13dbdae192449b34569
--- /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 0000000000000000000000000000000000000000..50b53829d1c7f6f5b17d7f44afa1c60aac1e4f7d
--- /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 0000000000000000000000000000000000000000..40eb96515fdb57eb1ccd15601bb31547c5996fe9
--- /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 306dde8135b5a7369248ce6433d5bac85432e040..e9c981ec0875ba8fe45c1d0f5c14cca54e0b4e5d 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 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/tf_model.ipynb b/tf_model.ipynb
index 0043c5fe5ef149c85387a31c0d3dd8301289c3b6..1256f64518b0b070fd4b622c378bd36a9844a4d8 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",