Mark input types and build a feature map

neural_network.py

@@ -9,7 +9,36 @@ 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.
@@ -23,7 +52,42 @@ 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')
@@ -31,4 +95,21 @@ for header in categorical_cols:
                                                max_tokens=5)
   encoded_categorical_col = encoding_layer(categorical_col)
   all_inputs.append(categorical_col)
-  encoded_features.append(encoded_categorical_col)
+  encoded_features.append(encoded_categorical_col)
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+
+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, '%')
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