Search Update

.streamlit/config.toml

@@ -1,3 +1,4 @@
 [theme]
 base='dark'
-font='sans serif'
+font='sans serif'
+

README.md

@@ -1,10 +1,10 @@
 # NBA Fantasy App
 
-This repository has a Streamlit app that lets you create your NBA fantasy team and compete against computer-generated teams. The app uses a Snowflake database that contains historical NBA playeer data.
+This repository has a Streamlit app that lets you create a NBA fantasy team and compete against computer-generated teams. The app uses a Snowflake database that contains historical NBA player data.
 
-To determine the winner of each game, the app employs a functional Keras model that was trained using the 2018 season's results. The model uses season averages in stats from the starting five players as features against thier wins and losses.
+To determine the winner of each game, the app uses a functional Keras model that was trained using the 2018 season's game by game results. The model uses season averages in stats from the starting five players as features against thier wins and losses. The training data and model script are included. 
 
-The app doesn't consider changes in playstyle or the three-point line.
+The app doesn't consider changes in playstyle or the three-point line when choosing from historical players.
 
 ## Game
 Play the game [here](https://hatmanstack-streamlit-nba-app-dz3nxx.streamlit.app/).
@@ -12,10 +12,10 @@ Play the game [here](https://hatmanstack-streamlit-nba-app-dz3nxx.streamlit.app/
 ## Usage
 To use this app, launch the Streamlit App using the following command install streamlit and then run it:
 
-```pip install streamlit```
-
-```streamlit run app.py```
-
+```
+pip install -r requirements.txt
+streamlit run app.py
+```
 
 ## License
 

compile_model.py

@@ -3,7 +3,8 @@ import numpy as np
 from tensorflow import keras
 from tensorflow.keras import layers
 from tensorflow.keras.losses import BinaryCrossentropy
-from sklearn.model_selection import train_test_split
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.neural_network import MLPClassifier
 
 def create_stats(roster, schedule):
     home_stats = []
@@ -32,8 +33,8 @@ schedule['winner'] = schedule.apply(lambda x: 0 if x['PTS'] > x['PTS.1'] else 1,
 
 X = np.array(create_stats(roster, schedule))
 y = np.array(schedule['winner'])
-
-inputs = keras.Input(shape=(100,))
+hidden_layer_sizes = (100,)
+inputs = keras.Input(shape=hidden_layer_sizes)
 dense = layers.Dense(50, activation="relu")
 x = dense(inputs)
 x = layers.Dense(64, activation="relu")(x)
@@ -48,8 +49,36 @@ model.compile(
 )
 
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-history = model.fit(X_train, y_train, batch_size=64, epochs=200, validation_split=0.2)
-model.save('my_model')
+model.fit(X_train, y_train, batch_size=64, epochs=500, validation_split=0.2)
+
 test_scores = model.evaluate(X_test, y_test, verbose=2)
 print("Test loss:", test_scores[0])
 print("Test accuracy:", test_scores[1])
+
+model.save('winner_model')
+# Define the parameter grid for grid search
+param_grid = {
+    'hidden_layer_sizes': [(50,), (100,), (50, 50), (100, 50)],
+    'activation': ['relu', 'tanh']
+}
+
+# Create the classifier (you can use any other model as well)
+classifier = MLPClassifier(max_iter=1000)
+
+# Initialize GridSearchCV
+grid_search = GridSearchCV(classifier, param_grid, cv=5, scoring='accuracy', verbose=1, n_jobs=-1)
+
+# Perform grid search
+grid_search.fit(X_train, y_train)
+
+# Get the best model
+best_model = grid_search.best_estimator_
+
+# Train the best model
+best_model.fit(X_train, y_train)
+
+# Evaluate the model
+test_scores = best_model.score(X_test, y_test)
+
+print("Best Parameters:", grid_search.best_params_)
+print("Test Accuracy:", test_scores)

pages/1_home_team.py

@@ -12,8 +12,9 @@ col1, col2, col3 = st.columns(3)
 with col2:
     st.markdown("<h1 style='text-align: center; color: steelblue;'>Build Your Team</h1>", unsafe_allow_html=True)
     player_add = st.text_input('Who\'re you picking?', 'James')
-
-search_string = 'select full_name, first_name, last_name from NBA where full_name=\'{}\' or first_name=\'{}\' or last_name=\'{}\';'.format(player_add, player_add, player_add)
+    player = player_add.lower()
+st.markdown("<p style='text-align: center; color: steelblue;'>Search for a player to populate the dropdown menu then pick and save your team before searching for another player.</p>", unsafe_allow_html=True)
+search_string = 'select full_name from NBA where full_name_lower=\'{}\' or first_name_lower=\'{}\' or last_name_lower=\'{}\';'.format(player, player, player)
 
 if 'home_team' not in st.session_state:
         st.session_state['home_team'] = []
@@ -48,7 +49,7 @@ def find_home_team():
             cur.execute('SELECT * FROM NBA WHERE FULL_NAME=\'{}\''.format(i))
             test.append(cur.fetchall()[0])
     cnx.close()    
-    df = pd.DataFrame(test, columns=['FULL_NAME', 'AST', 'BLK', 'DREB', 'FG3A', 'FG3M', 'FG3_PCT', 'FGA', 'FGM', 'FG_PCT', 'FTA', 'FTM', 'FT_PCT','GP', 'GS', 'MIN', 'OREB', 'PF', 'PTS', 'REB', 'STL', 'TOV', 'FIRST_NAME', 'LAST_NAME', 'IS_ACTIVE'])
+    df = pd.DataFrame(test, columns=['FULL_NAME', 'AST', 'BLK', 'DREB', 'FG3A', 'FG3M', 'FG3_PCT', 'FGA', 'FGM', 'FG_PCT', 'FTA', 'FTM', 'FT_PCT','GP', 'GS', 'MIN', 'OREB', 'PF', 'PTS', 'REB', 'STL', 'TOV', 'FIRST_NAME', 'LAST_NAME', 'FULL_NAME_LOWER', 'FIRST_NAME_LOWER', 'LAST_NAME_LOWER', 'IS_ACTIVE'])
     st.session_state.home_team_df = df
     return df
 
@@ -70,7 +71,7 @@ def save_state():
         for i in saved_players:
             if i not in player_selected:
                 st.session_state.home_team.remove(i)
-    st.experimental_rerun()
+    st.rerun()
 
 col1, col2 = st.columns([7,1])
 with col1:
@@ -91,27 +92,22 @@ with col3:
         label_visibility="collapsed", )
 
     if difficulty == 'Regular':
-        hard_string = 'Nancy'
         st.session_state.away_stats = [850, 400, 200, 60]
         st.session_state.radio_index = 0       
     elif difficulty == '93\' Bulls':
-        hard_string = 'True Fan'
         st.session_state.away_stats = [1050, 500, 300, 80]
         st.session_state.radio_index = 1
     elif difficulty == 'All-Stars':
-        hard_string = 'Getting Warmer'
         st.session_state.away_stats = [1250, 600, 400, 100]
         st.session_state.radio_index = 2
     elif difficulty == 'Dream Team':
-        hard_string = 'Stud'
         st.session_state.away_stats = [1450, 700, 500, 120]
         st.session_state.radio_index = 3
     else:
         st.write("You didn't select a difficulty.")
 
-    print(st.session_state.away_stats)
-    st.markdown("<h3 style='text-align: center; color: steelblue;'>{}</h3>".format(hard_string), unsafe_allow_html=True)
-
+    
+  
 
 
 

pages/2_play_game.py

@@ -31,7 +31,7 @@ def find_away_team():
     cnx = snowflake.connector.connect(**st.secrets["snowflake"])
     data = get_away_team(cnx, query_string)
     cnx.close()
-    df = pd.DataFrame(data, columns=['FULL_NAME', 'AST', 'BLK', 'DREB', 'FG3A', 'FG3M', 'FG3_PCT', 'FGA', 'FGM', 'FG_PCT', 'FTA', 'FTM', 'FT_PCT','GP', 'GS', 'MIN', 'OREB', 'PF', 'PTS', 'REB', 'STL', 'TOV', 'FIRST_NAME', 'LAST_NAME', 'IS_ACTIVE'])
+    df = pd.DataFrame(data, columns=['FULL_NAME', 'AST', 'BLK', 'DREB', 'FG3A', 'FG3M', 'FG3_PCT', 'FGA', 'FGM', 'FG_PCT', 'FTA', 'FTM', 'FT_PCT','GP', 'GS', 'MIN', 'OREB', 'PF', 'PTS', 'REB', 'STL', 'TOV', 'FIRST_NAME', 'LAST_NAME', 'FULL_NAME_LOWER', 'FIRST_NAME_LOWER', 'LAST_NAME_LOWER', 'IS_ACTIVE'])
     return df   
 
 if not st.session_state.home_team_df.shape[0] == 5:
@@ -84,9 +84,10 @@ if teams_good:
         loser_score = random.randint(80, 120)
         
 
-    if winner_prediction > 100:
+    if winner_prediction < 200:
         score.append(get_score_board(winner_prediction, winner_score))
         score.append(get_score_board(away_point_prediction, loser_score))
+        print(winner_prediction)
         winner = 'Winner'
     else:
         score.append(get_score_board(winner_prediction, loser_score))

requirements.txt

@@ -1,4 +1,4 @@
-snowflake-connector-python
+snowflake-connector-python==2.8.3
 tensorflow
 numpy
 pandas

winner_model/fingerprint.pb

@@ -0,0 +1 @@
+���������
�����ΏϨ
������� э�������
(��ۓ岩W2

winner_model/keras_metadata.pb

@@ -1,8 +1,8 @@
 
-�+root"_tf_keras_network*�*{"name": "nba_model", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": false, "class_name": "Functional", "config": {"name": "nba_model", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 100]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "name": "input_1", "inbound_nodes": []}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 50, "activation": "relu", "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}, "name": "dense", "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "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}, "name": "dense_1", "inbound_nodes": [[["dense", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 1, "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}, "name": "dense_2", "inbound_nodes": [[["dense_1", 0, 0, {}]]]}], "input_layers": [["input_1", 0, 0]], "output_layers": [["dense_2", 0, 0]]}, "shared_object_id": 10, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 100]}, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 100]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 100]}, "float32", "input_1"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 100]}, "float32", "input_1"]}, "keras_version": "2.10.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "nba_model", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 100]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "name": "input_1", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 50, "activation": "relu", "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}, "name": "dense", "inbound_nodes": [[["input_1", 0, 0, {}]]], "shared_object_id": 3}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_1", "inbound_nodes": [[["dense", 0, 0, {}]]], "shared_object_id": 6}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 7}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 8}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_2", "inbound_nodes": [[["dense_1", 0, 0, {}]]], "shared_object_id": 9}], "input_layers": [["input_1", 0, 0]], "output_layers": [["dense_2", 0, 0]]}}, "training_config": {"loss": {"class_name": "BinaryCrossentropy", "config": {"reduction": "auto", "name": "binary_crossentropy", "from_logits": true, "label_smoothing": 0.0, "axis": -1}, "shared_object_id": 12}, "metrics": [[{"class_name": "MeanMetricWrapper", "config": {"name": "accuracy", "dtype": "float32", "fn": "binary_accuracy"}, "shared_object_id": 13}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "RMSprop", "config": {"name": "RMSprop", "learning_rate": 0.0010000000474974513, "decay": 0.0, "rho": 0.8999999761581421, "momentum": 0.0, "epsilon": 1e-07, "centered": false}}}}2

+�-root"_tf_keras_network*�,{"name": "nba_model", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": false, "class_name": "Functional", "config": {"name": "nba_model", "trainable": true, "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 100]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "name": "input_1", "inbound_nodes": []}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 50, "activation": "relu", "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}, "name": "dense", "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "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}, "name": "dense_1", "inbound_nodes": [[["dense", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 1, "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}, "name": "dense_2", "inbound_nodes": [[["dense_1", 0, 0, {}]]]}], "input_layers": [["input_1", 0, 0]], "output_layers": [["dense_2", 0, 0]]}, "shared_object_id": 10, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 100]}, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 100]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 100]}, "float32", "input_1"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 100]}, "float32", "input_1"]}, "keras_version": "2.14.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "nba_model", "trainable": true, "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 100]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "name": "input_1", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 50, "activation": "relu", "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}, "name": "dense", "inbound_nodes": [[["input_1", 0, 0, {}]]], "shared_object_id": 3}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_1", "inbound_nodes": [[["dense", 0, 0, {}]]], "shared_object_id": 6}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 7}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 8}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_2", "inbound_nodes": [[["dense_1", 0, 0, {}]]], "shared_object_id": 9}], "input_layers": [["input_1", 0, 0]], "output_layers": [["dense_2", 0, 0]]}}, "training_config": {"loss": {"class_name": "BinaryCrossentropy", "config": {"reduction": "auto", "name": "binary_crossentropy", "from_logits": true, "label_smoothing": 0.0, "axis": -1, "fn": "binary_crossentropy"}, "shared_object_id": 12}, "metrics": [[{"class_name": "MeanMetricWrapper", "config": {"name": "accuracy", "dtype": "float32", "fn": "binary_accuracy"}, "shared_object_id": 13}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "Custom>RMSprop", "config": {"name": "RMSprop", "weight_decay": null, "clipnorm": null, "global_clipnorm": null, "clipvalue": null, "use_ema": false, "ema_momentum": 0.99, "ema_overwrite_frequency": 100, "jit_compile": false, "is_legacy_optimizer": false, "learning_rate": 0.0010000000474974513, "rho": 0.9, "momentum": 0.0, "epsilon": 1e-07, "centered": false}}}}2

 �
root.layer-0"_tf_keras_input_layer*�{"class_name": "InputLayer", "name": "input_1", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 100]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 100]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}}2

 �root.layer_with_weights-0"_tf_keras_layer*�{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 50, "activation": "relu", "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}, "inbound_nodes": [[["input_1", 0, 0, {}]]], "shared_object_id": 3, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 100}}, "shared_object_id": 14}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 100]}}2

 �root.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, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense", 0, 0, {}]]], "shared_object_id": 6, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 50}}, "shared_object_id": 15}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 50]}}2

 �root.layer_with_weights-2"_tf_keras_layer*�{"name": "dense_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 7}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 8}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_1", 0, 0, {}]]], "shared_object_id": 9, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 64}}, "shared_object_id": 16}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 64]}}2

-�
Nroot.keras_api.metrics.0"_tf_keras_metric*�
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 17}2

-�
Oroot.keras_api.metrics.1"_tf_keras_metric*�
{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "binary_accuracy"}, "shared_object_id": 13}2

+�
Mroot.keras_api.metrics.0"_tf_keras_metric*�
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 17}2

+�
Nroot.keras_api.metrics.1"_tf_keras_metric*�
{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "binary_accuracy"}, "shared_object_id": 13}2