--- a
+++ b/Models/main-Transformer.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+@Author: Bruce Shuyue Jia
+@Date: Jan 30, 2021
+"""
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+# Read Training Data
+train_data = pd.read_csv('training_set.csv', header=None)
+train_data = np.array(train_data).astype('float32')
+
+# Read Training Labels
+train_labels = pd.read_csv('training_label.csv', header=None)
+train_labels = np.array(train_labels).astype('float32')
+train_labels = np.squeeze(train_labels)
+
+# Read Testing Data
+test_data = pd.read_csv('test_set.csv', header=None)
+test_data = np.array(test_data).astype('float32')
+
+# Read Testing Labels
+test_labels = pd.read_csv('test_label.csv', header=None)
+test_labels = np.array(test_labels).astype('float32')
+test_labels = np.squeeze(test_labels)
+
+
+class TransformerBlock(layers.Layer):
+    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.5):
+        super(TransformerBlock, self).__init__()
+        self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
+        self.ffn = keras.Sequential([layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim), ])
+        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
+        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
+        self.dropout1 = layers.Dropout(rate)
+        self.dropout2 = layers.Dropout(rate)
+
+    def call(self, inputs, training):
+        attn_output = self.att(inputs, inputs)
+        attn_output = self.dropout1(attn_output, training=training)
+        out1 = self.layernorm1(inputs + attn_output)
+        ffn_output = self.ffn(out1)
+        ffn_output = self.dropout2(ffn_output, training=training)
+        out = self.layernorm2(out1 + ffn_output)
+        return out
+
+
+class TokenAndPositionEmbedding(layers.Layer):
+    def __init__(self, maxlen, embed_dim):
+        super(TokenAndPositionEmbedding, self).__init__()
+        self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=embed_dim)
+
+    def call(self, x):
+        positions = tf.range(start=0, limit=maxlen, delta=1)
+        positions = self.pos_emb(positions)
+        x = tf.reshape(x, [-1, maxlen, embed_dim])
+        out = x + positions
+        return out
+
+
+maxlen = 3      # Only consider 3 input time points
+embed_dim = 97  # Features of each time point
+num_heads = 8   # Number of attention heads
+ff_dim = 64     # Hidden layer size in feed forward network inside transformer
+
+# Input Time-series
+inputs = layers.Input(shape=(maxlen*embed_dim,))
+embedding_layer = TokenAndPositionEmbedding(maxlen, embed_dim)
+x = embedding_layer(inputs)
+
+# Encoder Architecture
+transformer_block_1 = TransformerBlock(embed_dim=embed_dim, num_heads=num_heads, ff_dim=ff_dim)
+transformer_block_2 = TransformerBlock(embed_dim=embed_dim, num_heads=num_heads, ff_dim=ff_dim)
+x = transformer_block_1(x)
+x = transformer_block_2(x)
+
+# Output
+x = layers.GlobalMaxPooling1D()(x)
+x = layers.Dropout(0.5)(x)
+x = layers.Dense(64, activation="relu")(x)
+x = layers.Dropout(0.5)(x)
+outputs = layers.Dense(1, activation="sigmoid")(x)
+
+model = keras.Model(inputs=inputs, outputs=outputs)
+
+model.compile(optimizer=tf.keras.optimizers.Adam(lr=1e-4),
+              loss="binary_crossentropy",
+              metrics=[tf.keras.metrics.Precision(), tf.keras.metrics.BinaryAccuracy(), tf.keras.metrics.Recall()])
+
+history = model.fit(
+    train_data, train_labels, batch_size=128, epochs=1000, validation_data=(test_data, test_labels)
+)