Added model and pipeline

README.md

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 ---
 license: mit
+pipeline_tag: image-classification
+library_name: generic
 ---

config.json

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+{}

ingredients_metadata.json

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"carbs": 3.6999999999999997}, "524": {"name": "parsley", "energy": 37.1, "fat": 0.8, "protein": 2.9000000000000004, "carbs": 6.4}, "525": {"name": "bok choy", "energy": 13.0, "fat": 0.2, "protein": 1.5, "carbs": 2.1999999999999997}, "526": {"name": "mustard greens", "energy": 27.0, "fat": 0.4, "protein": 2.9000000000000004, "carbs": 4.7}, "527": {"name": "chive", "energy": 30.0, "fat": 0.7000000000000001, "protein": 3.3000000000000003, "carbs": 4.3999999999999995}, "528": {"name": "celery root", "energy": 42.0, "fat": 0.3, "protein": 1.5, "carbs": 9.2}, "529": {"name": "chayote squash", "energy": 24.0, "fat": 0.5, "protein": 0.6, "carbs": 5.1}, "530": {"name": "endive", "energy": 17.0, "fat": 0.2, "protein": 1.3, "carbs": 3.4000000000000004}, "531": {"name": "pumpkin seeds", "energy": 446.0, "fat": 19.0, "protein": 19.0, "carbs": 54.0}, "532": {"name": "pesto", "energy": 562.0, "fat": 54.900000000000006, "protein": 12.1, "carbs": 8.0}, "533": {"name": "orzo", "energy": 225.0, "fat": 0.8999999999999999, "protein": 7.9, "carbs": 45.800000000000004}, "534": {"name": "country rice", "energy": 152.04, "fat": 6.800000000000001, "protein": 3.8600000000000003, "carbs": 31.6}, "535": {"name": "green onions", "energy": 32.0, "fat": 0.2, "protein": 1.7999999999999998, "carbs": 7.000000000000001}, "536": {"name": "banana with peel", "energy": 55.60000000000001, "fat": 0.2, "protein": 0.7000000000000001, "carbs": 14.399999999999999}, "537": {"name": "orange with peel", "energy": 29.4, "fat": 0.1, "protein": 0.6, "carbs": 7.5}, "538": {"name": "jicama", "energy": 38.0, "fat": 0.1, "protein": 0.7000000000000001, "carbs": 9.0}, "539": {"name": "nopales", "energy": 16.0, "fat": 0.1, "protein": 1.4000000000000001, "carbs": 3.3000000000000003}, "540": {"name": "ginger", "energy": 80.0, "fat": 0.8, "protein": 1.7999999999999998, "carbs": 18.0}, "541": {"name": "tomatillo", "energy": 32.0, "fat": 1.0, "protein": 1.0, "carbs": 6.0}, "542": {"name": "white beans", "energy": 139.0, "fat": 0.4, "protein": 9.700000000000001, "carbs": 25.1}, "543": {"name": "chia seeds", "energy": 486.00000000000006, "fat": 31.0, "protein": 17.0, "carbs": 42.0}, "544": {"name": "corn starch", "energy": 380.0, "fat": 0.05, "protein": 0.3, "carbs": 91.4}, "545": {"name": "greek yogurt", "energy": 59.0, "fat": 0.4, "protein": 10.0, "carbs": 3.5999999999999996}, "546": {"name": "balsamic vinegar", "energy": 88.0, "fat": 0.0, "protein": 0.5, "carbs": 17.0}, "547": {"name": "corn nuts", "energy": 436.00000000000006, "fat": 14.000000000000002, "protein": 9.0, "carbs": 72.0}, "548": {"name": "coconut milk", "energy": 229.99999999999997, "fat": 24.0, "protein": 2.3, "carbs": 6.0}, "549": {"name": "pizza dough", "energy": 235.5, "fat": 4.7, "protein": 5.4, "carbs": 41.699999999999996}, "550": {"name": "fennel", "energy": 31.0, "fat": 0.2, "protein": 1.2, "carbs": 7.000000000000001}, "551": {"name": "pizza sauce", "energy": 28.999999999999996, "fat": 0.2, "protein": 1.3, "carbs": 7.000000000000001}, "552": {"name": "pomegranate", "energy": 83.0, "fat": 1.2, "protein": 1.7000000000000002, "carbs": 19.0}, "553": {"name": "nut cheese", "energy": 210.0, "fat": 10.100000000000001, "protein": 25.6, "carbs": 4.0}, "554": {"name": "soy sausage", "energy": 202.0, "fat": 14.399999999999999, "protein": 16.0, "carbs": 6.3}, "555": {"name": "oil", "energy": 886.4000000000001, "fat": 100.0, "protein": 0.0, "carbs": 0.0}}

pipeline.py

@@ -0,0 +1,177 @@
+from typing import Dict, List, Any
+from PIL import Image
+from tfing import TFIng
+from tfport import TFPort, get_look_ahead_mask, get_padding_mask
+
+import os
+import json
+import tensorflow as tf
+import numpy as np
+
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        crop_size = (224, 224)
+        embed_dim = 256, 
+        num_layers = 3
+        seq_length = 20
+        hidden_dim = 1024
+        num_heads = 8
+        self.nutr_names = ('energy', 'fat', 'protein', 'carbs')
+        with open(f'ingredients_metadata.json', encoding='UTF-8') as f:
+            self.ingredients = json.load(f)
+        self.ing_names = {ing['name']: int(ing_id) for ing_id, ing in self.ingredients.items()}
+        self.vocab_size = len(self.ingredients) + 3
+        self.seq_length = seq_length
+
+        self.tfing = TFIng(
+            crop_size, 
+            embed_dim, 
+            num_layers, 
+            seq_length, 
+            hidden_dim, 
+            num_heads, 
+            self.vocab_size
+        )
+        self.tfing.compile()
+        self.tfing((tf.zeros((1, 224, 224, 3)), tf.zeros((1, seq_length))))
+        self.tfing.load_weights(f'tfing.h5')
+
+        self.tfport = TFPort(
+            crop_size, 
+            embed_dim, 
+            num_layers, 
+            num_layers, 
+            seq_length, 
+            seq_length, 
+            hidden_dim, 
+            num_heads, 
+            self.vocab_size
+        )
+        self.tfport.compile()
+        self.tfport((tf.zeros((1, 224, 224, 3)), tf.zeros((1, seq_length)), tf.zeros((1, seq_length))))
+        self.tfport.load_weights(f'/tfport.h5')
+
+    def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]:
+        image = tf.keras.preprocessing.image.img_to_array(inputs)
+        height = tf.shape(image)[0]
+        width = tf.shape(image)[1]
+        if width > height:
+            image = tf.image.resize(image, (self.img_size, int(float(self.img_size * width) / float(height))))
+        else:
+            image = tf.image.resize(image, (int(float(self.img_size * height) / float(width)), self.img_size))
+
+        image = tf.keras.applications.inception_v3.preprocess_input(image)
+        image = tf.keras.layers.CenterCrop(*self.crop_size)(image)
+        prediction = self.predict(image)
+        return [
+            {
+                "label": prediction['ingredients'][i], 
+                "score": prediction['portions'][i]
+            } 
+            for i in range(len(prediction['ingredients']))
+        ]
+
+    def encode_image(self, image):
+            encoder_out = self.tfing.encoder(image)
+            encoder_out = self.tfing.conv(encoder_out)
+            encoder_out = tf.reshape(
+                encoder_out, 
+                (tf.shape(encoder_out)[0], -1, tf.shape(encoder_out)[3])
+            )
+            return encoder_out
+
+    def encode_ingredients(self, ingredients, padding_mask):
+        return self.tfport.ingredient_encoder(ingredients, padding_mask)
+
+    def decode_ingredients(self, encoded_img, decoder_in):
+        decoder_outputs = self.tfing.decoder(decoder_in, encoded_img)
+        output = self.tfing.linear(decoder_outputs)
+        return output + self.tfing.get_replacement_mask(decoder_in)
+
+    def decode_portions(self, encoded_img, encoded_ingr, decoder_in, padding_mask):
+        encoder_outputs = tf.concat([encoded_img, encoded_ingr], axis=1)
+        img_mask = tf.ones((tf.shape(encoded_img)[0], 1, tf.shape(encoded_img)[1]), dtype=tf.int32)
+        padding_mask = tf.concat([img_mask, padding_mask], axis=2)
+        look_ahead_mask = get_look_ahead_mask(decoder_in)
+        
+        x = self.tfport.portion_embedding(decoder_in)
+        for i in range(len(self.tfport.decoder_layers)):
+            x = self.tfport.decoder_layers[i](x, encoder_outputs, look_ahead_mask, padding_mask=padding_mask)
+        x = self.tfport.linear(x)
+        return tf.squeeze(x)
+
+    def predict_ingredients(self, encoded_img, known_ing=None):
+        predicted = np.zeros((1, self.seq_length + 1), dtype=int)
+        predicted[0, 0] = self.vocab_size - 2
+        start_index = 0
+        if known_ing:
+            predicted[0, 1:len(known_ing) + 1] = known_ing
+            start_index = len(known_ing)
+        for i in range(start_index, self.seq_length):
+            decoded = self.decode_ingredients(encoded_img, predicted[:, :-1])
+            next_token = int(np.argmax(decoded[0, i]))
+            predicted[0, i + 1] = next_token
+            if next_token == self.vocab_size - 1:
+                return predicted[0, 1:]
+            if i == self.seq_length - 1:
+                predicted[0, i + 1] = self.vocab_size - 1
+                return predicted[0, 1:]
+
+    def predict_portions(self, encoded_image, ingredients):
+        predicted = np.zeros((1, self.seq_length + 1), dtype=float)
+        predicted[0, 0] = -1
+        padding_mask = get_padding_mask(ingredients)
+        encoded_ingr = self.encode_ingredients(ingredients, padding_mask)
+        for i in range(self.seq_length):
+            if ingredients[0, i] == self.vocab_size - 1:
+                return predicted[0, 1:]
+            next_proportion = float(
+                self.decode_portions(
+                    encoded_image, 
+                    encoded_ingr, 
+                    predicted[:, :-1], 
+                    padding_mask
+                )[i]
+            )
+            predicted[0, i + 1] = next_proportion
+        return predicted[0, 1:]
+
+    def process_ingredients(self, ingredients):
+        processed = []
+        for ingredient in ingredients.split('\n'):
+            stripped = ingredient.strip()
+            if stripped == '.':
+                return processed, True
+            if stripped in self.ing_names:
+                processed.append(self.ing_names[stripped])
+        return processed, False
+
+    def predict(self, image, known_ing=None):
+        encoded_image = self.encode_image(image[tf.newaxis, :])
+        known_ing, skip_ing = self.process_ingredients(known_ing)\
+            if known_ing else (None, False)
+        if not skip_ing:
+            ingredients = self.predict_ingredients(encoded_image, known_ing=known_ing)
+        else:
+            ingredients = known_ing[:self.seq_length - 1]
+            ingredients.append(self.vocab_size - 1)
+            ingredients = np.pad(ingredients, (0, self.seq_length - len(ingredients)))
+        readable_ingredients = [
+            self.ingredients[str(token)]['name'] for token in ingredients 
+            if token != 0 and token != self.vocab_size - 1
+        ]
+        portions = self.predict_portions(encoded_image, ingredients[tf.newaxis, :])\
+            if len(readable_ingredients) > 1 else [100]
+        portions_slice = portions[:len(readable_ingredients)]
+        scale = 100 / sum(portions_slice)
+        return {
+            'ingredients': readable_ingredients,
+            'portions': [portion * scale for portion in portions_slice],
+            'nutrition': {
+                name: sum(
+                    self.ingredients[str(ingredients[i])][name] * portions[i] / 100 
+                    for i in range(len(readable_ingredients))
+                ) for name in self.nutr_names
+            }
+        }

requirements.txt

@@ -0,0 +1,3 @@
+tensorflow==2.9.0
+tensorflow_probability
+numpy

tfing.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f0f9c7d524719bc922424f1642397d6203cd7aeafc8b19d1e8c03f241df0a69
+size 147729928

tfing.py

@@ -0,0 +1,71 @@
+import tensorflow as tf
+from tensorflow import keras
+from .transformer import TransformerDecoder
+import tensorflow_probability as tfp
+
+class TFIng(keras.Model):
+    def __init__(self, crop_size, embed_dim, num_layers, seq_length, hidden_dim, num_heads, 
+                    target_vocab_size, dropout_rate=0.1):
+        super().__init__()
+        self.target_vocab_size = target_vocab_size
+
+        self.encoder = keras.applications.InceptionV3(
+            include_top=False, 
+            weights="imagenet", 
+            input_shape=crop_size + (3,),
+        )
+        self.conv = keras.layers.Conv2D(embed_dim, 1)
+        self.decoder = TransformerDecoder(num_layers, seq_length, embed_dim, hidden_dim, num_heads, 
+                                            target_vocab_size, dropout_rate=dropout_rate)
+        self.linear = keras.layers.Dense(target_vocab_size, activation=None)
+
+    def call(self, inputs, training=False):
+        encoder_inputs, targets = inputs
+        encoder_out = self.encoder(encoder_inputs, training=training)
+        encoder_out = self.conv(encoder_out, training=training)
+        encoder_out = tf.reshape(encoder_out, (tf.shape(encoder_out)[0], -1, tf.shape(encoder_out)[3]))
+        decoder_outputs = self.decoder(targets, encoder_out, training=training)
+        output = self.linear(decoder_outputs)
+        return output + self.get_replacement_mask(targets)
+
+    def get_replacement_mask(self, targets):
+        targets = tf.cast(targets, tf.int32)
+        batch_size, seq_length = tf.shape(targets)[0], tf.shape(targets)[1]
+
+        n = int(seq_length * (seq_length  + 1) / 2)
+        mask = tfp.math.fill_triangular(tf.ones((n,), dtype=tf.int32))
+        mask = tf.repeat(mask[tf.newaxis, :], batch_size, axis=0)
+
+        targets_repeated = tf.repeat(targets[:, tf.newaxis, :], seq_length, axis=1)
+        targets_masked = targets_repeated * mask
+        columns = tf.boolean_mask(
+            targets_masked, 
+            tf.where(targets_masked != 0, tf.ones_like(targets_masked), tf.zeros_like(targets_masked))
+        )
+
+        rows_idx = tf.range(seq_length)
+        rows_idx_repeated = tf.reshape(tf.repeat(rows_idx, seq_length), (seq_length, seq_length))
+        rows_idx_repeated = tf.repeat(rows_idx_repeated[tf.newaxis, :], batch_size, axis=0)
+        rows = tf.boolean_mask(
+            rows_idx_repeated, 
+            tf.where(targets_masked != 0, tf.ones_like(targets_masked), tf.zeros_like(targets_masked))
+        )
+
+        batches_idx = tf.range(batch_size)
+        batches_idx_repeated = tf.reshape(
+            tf.repeat(batches_idx, seq_length * seq_length), (batch_size, seq_length, seq_length)
+        )
+        batches = tf.boolean_mask(
+            batches_idx_repeated, 
+            tf.where(targets_masked != 0, tf.ones_like(targets_masked), tf.zeros_like(targets_masked))
+        )
+
+        idx = tf.stack([batches, rows, columns], axis=1)
+
+        sparse_mask = tf.SparseTensor(
+            tf.cast(idx, tf.int64), 
+            tf.fill([tf.shape(idx)[0]], float('-inf')), 
+            [batch_size, seq_length, self.target_vocab_size]
+        )
+        sparse_mask = tf.sparse.reorder(sparse_mask)
+        return tf.sparse.to_dense(sparse_mask)

tfport.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52942aabb881f05d38bb960a7d083cf2b0929bde563264f974114355ce229b15
+size 178806824

tfport.py

@@ -0,0 +1,62 @@
+import tensorflow as tf
+from tensorflow import keras
+from .transformer import TransformerEncoder, TransformerDecoderLayer, get_look_ahead_mask, get_padding_mask
+
+class TFPort(keras.Model):
+    def __init__(self, crop_size, embed_dim, encoder_layers, decoder_layers, input_seq_length, 
+                    target_seq_length, hidden_dim, num_heads, input_vocab_size, dropout_rate=0.1):
+        super().__init__()
+
+        self.image_encoder = keras.applications.InceptionV3(
+            include_top=False, 
+            weights='imagenet',
+            input_shape=crop_size + (3,),
+        )
+        self.image_encoder.trainable = False
+        self.conv = keras.layers.Conv2D(embed_dim, 1)
+        self.ingredient_encoder = TransformerEncoder(encoder_layers, input_seq_length, embed_dim, hidden_dim, 
+                                                        num_heads, input_vocab_size, dropout_rate=dropout_rate)
+        self.portion_embedding = PortionEmbedding(target_seq_length, embed_dim)
+        self.dropout = keras.layers.Dropout(dropout_rate)
+        self.decoder_layers = [
+            TransformerDecoderLayer(embed_dim, hidden_dim, num_heads, dropout_rate=dropout_rate)
+            for _ in range(decoder_layers)
+        ]
+        self.linear = keras.layers.Dense(1, activation="relu")
+
+    def call(self, inputs, training=False):
+        image, ingredients, targets = inputs
+        padding_mask = get_padding_mask(ingredients)
+        encoded_img = self.image_encoder(image, training=False)
+        encoded_img = self.conv(encoded_img, training=training)
+        encoded_img = tf.reshape(encoded_img, (tf.shape(encoded_img)[0], -1, tf.shape(encoded_img)[3]))
+        encoded_ingr = self.ingredient_encoder(ingredients, padding_mask, training=training)
+        encoder_outputs = tf.concat([encoded_img, encoded_ingr], axis=1)
+
+        img_mask = tf.ones((tf.shape(encoded_img)[0], 1, tf.shape(encoded_img)[1]), dtype=tf.int32)
+        padding_mask = tf.concat([img_mask, padding_mask], axis=2)
+        look_ahead_mask = get_look_ahead_mask(targets)
+        
+        x = self.portion_embedding(targets)
+        x = self.dropout(x, training=training)
+        for i in range(len(self.decoder_layers)):
+            x = self.decoder_layers[i](x, encoder_outputs, look_ahead_mask, training=training, 
+                                        padding_mask=padding_mask)
+        x = self.linear(x)
+        return tf.squeeze(x)
+
+
+class PortionEmbedding(keras.layers.Layer):
+    def __init__(self, seq_length, embed_dim):
+        super().__init__()
+
+        self.linear = keras.layers.Dense(embed_dim)
+        self.position_embeddings = keras.layers.Embedding(
+            input_dim=seq_length, output_dim=embed_dim
+        )
+
+    def call(self, inputs):
+        positions = tf.range(start=0, limit=tf.shape(inputs)[-1], delta=1)
+        embedded_portions = self.linear(inputs[:, :, tf.newaxis])
+        embedded_positions = self.position_embeddings(positions)
+        return embedded_portions + embedded_positions

transformer.py

@@ -0,0 +1,179 @@
+import tensorflow as tf
+import tensorflow_probability as tfp
+from tensorflow import keras
+
+class TransformerEncoderLayer(keras.layers.Layer):
+    def __init__(self, embed_dim, hidden_dim, num_heads, dropout_rate=0.1):
+        super().__init__()
+
+        self.attention = keras.layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim
+        )
+        self.feed_forward = keras.Sequential(
+            [
+                keras.layers.Dense(hidden_dim, activation="relu"), 
+                keras.layers.Dense(embed_dim, activation=None)
+            ]
+        )
+
+        self.layernorm1 = keras.layers.LayerNormalization()
+        self.layernorm2 = keras.layers.LayerNormalization()
+        
+        self.dropout1 = keras.layers.Dropout(dropout_rate)
+        self.dropout2 = keras.layers.Dropout(dropout_rate)
+
+    def call(self, inputs, padding_mask, training=False):
+        attn_out = self.attention(
+            query=inputs, 
+            value=inputs, 
+            key=inputs, 
+            attention_mask=padding_mask
+        )
+        attn_out = self.dropout1(attn_out, training=training)
+        x = self.layernorm1(inputs + attn_out)
+
+        ff_out = self.feed_forward(x)
+        ff_out = self.dropout2(ff_out, training=training)
+        return self.layernorm2(x + ff_out)
+
+
+class TransformerEncoder(keras.Model):
+    def __init__(self, num_layers, seq_length, embed_dim, hidden_dim, num_heads, vocab_size, 
+                dropout_rate=0.1):
+        super().__init__()
+
+        self.embedding = PositionalEmbedding(seq_length, vocab_size, embed_dim)
+        self.dropout = keras.layers.Dropout(dropout_rate)
+        self.encoder_layers = [
+            TransformerEncoderLayer(embed_dim, hidden_dim, num_heads, dropout_rate=dropout_rate)
+            for _ in range(num_layers)
+        ]
+
+    def call(self, inputs, padding_mask, training=False):
+        x = self.embedding(inputs)
+        x = self.dropout(x, training=training)
+        for i in range(len(self.encoder_layers)):
+            x = self.encoder_layers[i](x, padding_mask, training=training)
+        return x
+
+
+class TransformerDecoderLayer(keras.layers.Layer):
+    def __init__(self, embed_dim, hidden_dim, num_heads, dropout_rate=0.1):
+        super().__init__()
+
+        self.self_attention = keras.layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim
+        )
+        self.attention = keras.layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim
+        )
+        self.feed_fordward = keras.Sequential(
+            [
+                keras.layers.Dense(hidden_dim, activation="relu"), 
+                keras.layers.Dense(embed_dim, activation=None)
+            ]
+        )
+        
+        self.layernorm1 = keras.layers.LayerNormalization()
+        self.layernorm2 = keras.layers.LayerNormalization()
+        self.layernorm3 = keras.layers.LayerNormalization()
+
+        self.dropout1 = keras.layers.Dropout(dropout_rate)
+        self.dropout2 = keras.layers.Dropout(dropout_rate)
+        self.dropout3 = keras.layers.Dropout(dropout_rate)
+
+    def call(self, inputs, encoder_outputs, look_ahead_mask, training=False, padding_mask=None):
+        self_attn_out = self.self_attention(
+            query=inputs, 
+            value=inputs, 
+            key=inputs, 
+            attention_mask=look_ahead_mask
+        )
+        self_attn_out = self.dropout1(self_attn_out, training=training)
+        x = self.layernorm1(inputs + self_attn_out)
+
+        attn_out = self.attention(
+            query=x,
+            value=encoder_outputs,
+            key=encoder_outputs,
+            attention_mask=padding_mask
+        )
+        attn_out = self.dropout2(attn_out, training=training)
+        x = self.layernorm2(x + attn_out)
+
+        ff_out = self.feed_fordward(x)
+        ff_out = self.dropout3(ff_out, training=training)
+        return self.layernorm3(x + ff_out)
+
+
+class TransformerDecoder(keras.Model):
+    def __init__(self, num_layers, seq_length, embed_dim, hidden_dim, num_heads, vocab_size, 
+                dropout_rate=0.1):
+        super().__init__()
+
+        self.embedding = PositionalEmbedding(seq_length, vocab_size, embed_dim)
+        self.dropout = keras.layers.Dropout(dropout_rate)
+        self.decoder_layers = [
+            TransformerDecoderLayer(embed_dim, hidden_dim, num_heads, dropout_rate=dropout_rate)
+            for _ in range(num_layers)
+        ]
+
+    def call(self, inputs, encoder_outputs, training=False, padding_mask=None):
+        look_ahead_mask = get_look_ahead_mask(inputs)
+        x = self.embedding(inputs)
+        x = self.dropout(x, training=training)
+        for i in range(len(self.decoder_layers)):
+            x = self.decoder_layers[i](x, encoder_outputs, look_ahead_mask, 
+                                        training=training, padding_mask=padding_mask)
+        return x
+
+
+def get_padding_mask(inputs):
+    mask = tf.cast(tf.math.not_equal(inputs, 0), tf.int32)
+    return mask[:, tf.newaxis, :]
+
+def get_look_ahead_mask(inputs):
+    input_shape = tf.shape(inputs)
+    batch_size, seq_length = input_shape[0], input_shape[1]
+    n = int(seq_length * (seq_length  + 1) / 2)
+    mask = tfp.math.fill_triangular(tf.ones((n,), dtype=tf.int32))
+    mask = tf.repeat(mask[tf.newaxis, :], batch_size, axis=0)
+    return tf.minimum(mask, get_padding_mask(inputs))
+
+
+class PositionalEmbedding(keras.layers.Layer):
+    def __init__(self, seq_length, vocab_size, embed_dim):
+        super().__init__()
+
+        self.token_embeddings = keras.layers.Embedding(
+            input_dim=vocab_size, output_dim=embed_dim
+        )
+        self.position_embeddings = keras.layers.Embedding(
+            input_dim=seq_length, output_dim=embed_dim
+        )
+
+    def call(self, inputs):
+        positions = tf.range(start=0, limit=tf.shape(inputs)[-1], delta=1)
+        embedded_tokens = self.token_embeddings(inputs)
+        embedded_positions = self.position_embeddings(positions)
+        return embedded_tokens + embedded_positions
+
+
+class Transformer(keras.Model):
+    def __init__(self, encoder_layers, decoder_layers, input_seq_length, target_seq_length, embed_dim, 
+                    hidden_dim, num_heads, input_vocab_size, target_vocab_size, dropout_rate=0.1):
+        super().__init__()
+        
+        self.encoder = TransformerEncoder(encoder_layers, input_seq_length, embed_dim, hidden_dim, 
+                                            num_heads, input_vocab_size, dropout_rate=dropout_rate)
+        self.decoder = TransformerDecoder(decoder_layers, target_seq_length, embed_dim, hidden_dim, 
+                                            num_heads, target_vocab_size, dropout_rate=dropout_rate)
+        self.linear = keras.layers.Dense(target_vocab_size, activation=None)
+
+    def call(self, inputs, training=False):
+        encoder_inputs, targets = inputs
+        padding_mask = get_padding_mask(encoder_inputs)
+        encoder_outputs = self.encoder(encoder_inputs, padding_mask, training=training)
+        decoder_outputs = self.decoder(targets, encoder_outputs, training=training, 
+                                        padding_mask=padding_mask)
+        return self.linear(decoder_outputs)