Data normalization.py

neural_network.py

@@ -9,36 +9,7 @@ from tensorflow.keras.layers.experimental import preprocessing
 dataframe = pd.read_csv('Database_Final.csv')
 print(dataframe.head())
 
-dataframe['target'] = dataframe['Remaining space']
 
-dataframe = dataframe.drop(columns=['Unnamed: 0','Remaining space'])
-
-# dataframe.head()
-
-train, test = train_test_split(dataframe, test_size=0.2)
-train, val = train_test_split(train, test_size=0.2)
-print(len(train), 'train examples')
-print(len(val), 'validation examples')
-print(len(test), 'test examples')
-
-def df_to_dataset(dataframe, label_column, shuffle=True, batch_size=32):
-    dataframe = dataframe.copy()
-    labels = dataframe.pop(label_column)
-    #labels = dataframe[label_column]
-
-    ds = tf.data.Dataset.from_tensor_slices((dataframe.to_dict(orient='list'), labels))
-    if shuffle:
-        ds = ds.shuffle(buffer_size=len(dataframe))
-    ds = ds.batch(batch_size)
-    return ds
-
-batch_size = 5
-train_ds = df_to_dataset(train,'target',batch_size=batch_size)
-
-[(train_features, label_batch)] = train_ds.take(1)
-print('Every feature:', list(train_features.keys()))
-print('A batch of id:', train_features['ID'])
-print('A batch of targets:', label_batch )
 
 def get_normalization_layer(name, dataset):
   # Create a Normalization layer for our feature.
@@ -52,64 +23,3 @@ def get_normalization_layer(name, dataset):
 
   return normalizer
 
-def get_category_encoding_layer(name, dataset, dtype, max_tokens=None):
-  # Create a StringLookup layer which will turn strings into integer indices
-  if dtype == 'string':
-    index = preprocessing.StringLookup(max_tokens=max_tokens)
-  else:
-    index = preprocessing.IntegerLookup(max_tokens=max_tokens)
-
-  # Prepare a Dataset that only yields our feature
-  feature_ds = dataset.map(lambda x, y: x[name])
-
-  # Learn the set of possible values and assign them a fixed integer index.
-  index.adapt(feature_ds)
-
-  # Create a Discretization for our integer indices.
-  encoder = preprocessing.CategoryEncoding(num_tokens=index.vocabulary_size())
-
-  # Apply one-hot encoding to our indices. The lambda function captures the
-  # layer so we can use them, or include them in the functional model later.
-  return lambda feature: encoder(index(feature))
-
-batch_size = 256
-train_ds = df_to_dataset(train,'target', batch_size=batch_size)
-val_ds = df_to_dataset(val,'target', shuffle=False, batch_size=batch_size)
-test_ds = df_to_dataset(test,'target', shuffle=False, batch_size=batch_size)
-
-all_inputs = []
-encoded_features = []
-
-# Numeric features.
-for header in ['Entry', 'Exit']:
-  numeric_col = tf.keras.Input(shape=(1,), name=header)
-  normalization_layer = get_normalization_layer(header, train_ds)
-  encoded_numeric_col = normalization_layer(numeric_col)
-  all_inputs.append(numeric_col)
-  encoded_features.append(encoded_numeric_col)
-  
-categorical_cols = ['Date', 'Time', 'Weather', 'Parking']
-for header in categorical_cols:
-  categorical_col = tf.keras.Input(shape=(1,), name=header, dtype='string')
-  encoding_layer = get_category_encoding_layer(header, train_ds, dtype='string',
-                                               max_tokens=5)
-  encoded_categorical_col = encoding_layer(categorical_col)
-  all_inputs.append(categorical_col)
-  encoded_features.append(encoded_categorical_col)
-
-all_features = tf.keras.layers.concatenate(encoded_features)
-x = tf.keras.layers.Dense(64, activation="relu")(all_features)
-x = tf.keras.layers.Dropout(0.5)(x)
-output = tf.keras.layers.Dense(1)(x)
-model = tf.keras.Model(all_inputs, output)
-model.compile(optimizer='adam',
-              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
-              metrics=["accuracy"])
-
-# rankdir='LR' is used to make the graph horizontal.
-# tf.keras.utils.plot_model(model, show_shapes=True, rankdir="LR")
-
-model.fit(train_ds, epochs=1, validation_data=val_ds)
-
-loss, accuracy = model.evaluate(test_ds)
-print("Accuracy", accuracy*100, '%')
\ No newline at end of file