feat(Core): added the first classification model & the feature necessary (#4)

* feat(Core): added the first classification model & the feature necessary

* feat(Models): added basic transformers model

keras_models/classification_transformer.py

@@ -0,0 +1,41 @@
+from keras import layers
+import keras
+
+def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0):
+    # Normalization and Attention
+    x = layers.LayerNormalization(epsilon=1e-6)(inputs)
+    x = layers.MultiHeadAttention(
+        key_dim=head_size, num_heads=num_heads, dropout=dropout
+    )(x, x)
+    x = layers.Dropout(dropout)(x)
+    res = x + inputs
+
+    # Feed Forward Part
+    x = layers.LayerNormalization(epsilon=1e-6)(res)
+    x = layers.Conv1D(filters=ff_dim, kernel_size=1, activation="relu")(x)
+    x = layers.Dropout(dropout)(x)
+    x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)
+    return x + res
+
+def create_basic_transformer_model(
+    input_shape,
+    n_classes,
+    head_size,
+    num_heads,
+    ff_dim,
+    num_transformer_blocks,
+    mlp_units,
+    dropout=0,
+    mlp_dropout=0,
+):
+    inputs = keras.Input(shape=input_shape)
+    x = inputs
+    for _ in range(num_transformer_blocks):
+        x = transformer_encoder(x, head_size, num_heads, ff_dim, dropout)
+
+    x = layers.GlobalAveragePooling1D(data_format="channels_first")(x)
+    for dim in mlp_units:
+        x = layers.Dense(dim, activation="relu")(x)
+        x = layers.Dropout(mlp_dropout)(x)
+    outputs = layers.Dense(n_classes, activation="softmax")(x)
+    return keras.Model(inputs, outputs)