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Sequential retrieval using SASRec

Author: Abheesht Sharma, Fabien Hertschuh
Date created: 2025/04/28
Last modified: 2025/04/28
Description: Recommend movies using a Transformer-based retrieval model (SASRec).

ⓘ This example uses Keras 2

[**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/keras_rs/ipynb/sas_rec.ipynb) •

[**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/keras_rs/sas_rec.py)

Introduction

Sequential recommendation is a popular model that looks at a sequence of items that users have interacted with previously and then predicts the next item. Here, the order of the items within each sequence matters. Previously, in the Recommending movies: retrieval using a sequential model example, we built a GRU-based sequential retrieval model. In this example, we will build a popular Transformer decoder-based model named Self-Attentive Sequential Recommendation (SASRec) for the same sequential recommendation task.

Let's begin by importing all the necessary libraries.

import os

os.environ["KERAS_BACKEND"] = "jax"  # `"tensorflow"`/`"torch"`

import collections
import os

import keras
import keras_hub
import numpy as np
import pandas as pd
import tensorflow as tf  # Needed only for the dataset
from keras import ops

import keras_rs

Let's also define all important variables/hyperparameters below.

DATA_DIR = "./raw/data/"

# MovieLens-specific variables
MOVIELENS_1M_URL = "https://files.grouplens.org/datasets/movielens/ml-1m.zip"
MOVIELENS_ZIP_HASH = "a6898adb50b9ca05aa231689da44c217cb524e7ebd39d264c56e2832f2c54e20"

RATINGS_FILE_NAME = "ratings.dat"
MOVIES_FILE_NAME = "movies.dat"

# Data processing args
MAX_CONTEXT_LENGTH = 200
MIN_SEQUENCE_LENGTH = 3
PAD_ITEM_ID = 0

RATINGS_DATA_COLUMNS = ["UserID", "MovieID", "Rating", "Timestamp"]
MOVIES_DATA_COLUMNS = ["MovieID", "Title", "Genres"]
MIN_RATING = 2

# Training/model args picked from SASRec paper
BATCH_SIZE = 128
NUM_EPOCHS = 10
LEARNING_RATE = 0.001

NUM_LAYERS = 2
NUM_HEADS = 1
HIDDEN_DIM = 50
DROPOUT = 0.2

Dataset

Next, we need to prepare our dataset. Like we did in the sequential retrieval example, we are going to use the MovieLens dataset.

The dataset preparation step is fairly involved. The original ratings dataset contains (user, movie ID, rating, timestamp) tuples (among other columns, which are not important for this example). Since we are dealing with sequential retrieval, we need to create movie sequences for every user, where the sequences are ordered by timestamp.

Let's start by downloading and reading the dataset.

# Download the MovieLens dataset.
if not os.path.exists(DATA_DIR):
    os.makedirs(DATA_DIR)

path_to_zip = keras.utils.get_file(
    fname="ml-1m.zip",
    origin=MOVIELENS_1M_URL,
    file_hash=MOVIELENS_ZIP_HASH,
    hash_algorithm="sha256",
    extract=True,
    cache_dir=DATA_DIR,
)
movielens_extracted_dir = os.path.join(
    os.path.dirname(path_to_zip),
    "ml-1m_extracted",
    "ml-1m",
)


# Read the dataset.
def read_data(data_directory, min_rating=None):
    """Read movielens ratings.dat and movies.dat file
    into dataframe.
    """

    ratings_df = pd.read_csv(
        os.path.join(data_directory, RATINGS_FILE_NAME),
        sep="::",
        names=RATINGS_DATA_COLUMNS,
        encoding="unicode_escape",
    )
    ratings_df["Timestamp"] = ratings_df["Timestamp"].apply(int)

    # Remove movies with `rating < min_rating`.
    if min_rating is not None:
        ratings_df = ratings_df[ratings_df["Rating"] >= min_rating]

    movies_df = pd.read_csv(
        os.path.join(data_directory, MOVIES_FILE_NAME),
        sep="::",
        names=MOVIES_DATA_COLUMNS,
        encoding="unicode_escape",
    )
    return ratings_df, movies_df


ratings_df, movies_df = read_data(
    data_directory=movielens_extracted_dir, min_rating=MIN_RATING
)

# Need to know #movies so as to define embedding layers.
movies_count = movies_df["MovieID"].max()

``` Downloading data from https://files.grouplens.org/datasets/movielens/ml-1m.zip

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:26: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'. ratings_df = pd.read_csv(

:38: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'. movies_df = pd.read_csv(

</div>
Now that we have read the dataset, let's create sequences of movies
for every user. Here is the function for doing just that.


```python

def get_movie_sequence_per_user(ratings_df):
    """Get movieID sequences for every user."""
    sequences = collections.defaultdict(list)

    for user_id, movie_id, rating, timestamp in ratings_df.values:
        sequences[user_id].append(
            {
                "movie_id": movie_id,
                "timestamp": timestamp,
                "rating": rating,
            }
        )

    # Sort movie sequences by timestamp for every user.
    for user_id, context in sequences.items():
        context.sort(key=lambda x: x["timestamp"])
        sequences[user_id] = context

    return sequences


sequences = get_movie_sequence_per_user(ratings_df)

So far, we have essentially replicated what we did in the sequential retrieval example. We have a sequence of movies for every user.

SASRec is trained contrastively, which means the model learns to distinguish between sequences of movies a user has actually interacted with (positive examples) and sequences they have not interacted with (negative examples).

The following function, format_data, prepares the data in this specific format. For each user's movie sequence, it generates a corresponding "negative sequence". This negative sequence consists of randomly selected movies that the user has not interacted with, but are of the same length as the original sequence.


def format_data(sequences):
    examples = {
        "sequence": [],
        "negative_sequence": [],
    }

    for user_id in sequences:
        sequence = [int(d["movie_id"]) for d in sequences[user_id]]

        # Get negative sequence.
        def random_negative_item_id(low, high, positive_lst):
            sampled = np.random.randint(low=low, high=high)
            while sampled in positive_lst:
                sampled = np.random.randint(low=low, high=high)
            return sampled

        negative_sequence = [
            random_negative_item_id(1, movies_count + 1, sequence)
            for _ in range(len(sequence))
        ]

        examples["sequence"].append(np.array(sequence))
        examples["negative_sequence"].append(np.array(negative_sequence))

    examples["sequence"] = tf.ragged.constant(examples["sequence"])
    examples["negative_sequence"] = tf.ragged.constant(examples["negative_sequence"])

    return examples


examples = format_data(sequences)
ds = tf.data.Dataset.from_tensor_slices(examples).batch(BATCH_SIZE)

Now that we have the original movie interaction sequences for each user (from format_data, stored in examples["sequence"]) and their corresponding random negative sequences (in examples["negative_sequence"]), the next step is to prepare this data for input to the model. The primary goals of this preprocessing are:

Creating Input Features and Target Labels: For sequential recommendation, the model learns to predict the next item in a sequence given the preceding items. This is achieved by:
- taking the original example["sequence"] and creating the model's input features (item_ids) from all items except the last one (example["sequence"][..., :-1]);
- creating the target "positive sequence" (what the model tries to predict as the actual next items) by taking the original example["sequence"] and shifting it, using all items except the first one (example["sequence"][..., 1:]);
- shifting example["negative_sequence"] (from format_data) is to create the target "negative sequence" for the contrastive loss (example["negative_sequence"][..., 1:]).
Handling Variable Length Sequences: Neural networks typically require fixed-size inputs. Therefore, both the input feature sequences and the target sequences are padded (with a special PAD_ITEM_ID) or truncated to a predefined MAX_CONTEXT_LENGTH. A padding_mask is also generated from the input features to ensure the model ignores these padded tokens during attention calculations, i.e, these tokens will be masked.
Differentiating Training and Validation/Testing:
- During training:
  - Input features (item_ids) and context for negative sequences are prepared as described above (all but the last item of the original sequences).
  - Target positive and negative sequences are the shifted versions of the original sequences.
    - sample_weight is created based on the input features to ensure that loss is calculated only on actual items, not on padding tokens in the targets.
- During validation/testing:
  - Input features are prepared similarly.
  - The model's performance is typically evaluated on its ability to predict the actual last item of the original sequence. Thus, sample_weight is configured to focus the loss calculation only on this final prediction in the target sequences.

Note: SASRec does the same thing we've done above, except that they take the item_ids[:-2] for the validation set and item_ids[:-1] for the test set. We skip that here for brevity.


def _preprocess(example, train=False):
    sequence = example["sequence"]
    negative_sequence = example["negative_sequence"]

    if train:
        sequence = example["sequence"][..., :-1]
        negative_sequence = example["negative_sequence"][..., :-1]

    batch_size = tf.shape(sequence)[0]

    if not train:
        # Loss computed only on last token.
        sample_weight = tf.zeros_like(sequence, dtype="float32")[..., :-1]
        sample_weight = tf.concat(
            [sample_weight, tf.ones((batch_size, 1), dtype="float32")], axis=1
        )

    # Truncate/pad sequence. +1 to account for truncation later.
    sequence = sequence.to_tensor(
        shape=[batch_size, MAX_CONTEXT_LENGTH + 1], default_value=PAD_ITEM_ID
    )
    negative_sequence = negative_sequence.to_tensor(
        shape=[batch_size, MAX_CONTEXT_LENGTH + 1], default_value=PAD_ITEM_ID
    )
    if train:
        sample_weight = tf.cast(sequence != PAD_ITEM_ID, dtype="float32")
    else:
        sample_weight = sample_weight.to_tensor(
            shape=[batch_size, MAX_CONTEXT_LENGTH + 1], default_value=0
        )

    example = (
        {
            # last token does not have a next token
            "item_ids": sequence[..., :-1],
            # padding mask for controlling attention mask
            "padding_mask": (sequence != PAD_ITEM_ID)[..., :-1],
        },
        {
            "positive_sequence": sequence[
                ..., 1:
            ],  # 0th token's label will be 1st token, and so on
            "negative_sequence": negative_sequence[..., 1:],
        },
        sample_weight[..., 1:],  # loss will not be computed on pad tokens
    )
    return example


def preprocess_train(examples):
    return _preprocess(examples, train=True)


def preprocess_val(examples):
    return _preprocess(examples, train=False)


train_ds = ds.map(preprocess_train)
val_ds = ds.map(preprocess_val)

We can see a batch for each.

for batch in train_ds.take(1):
    print(batch)

for batch in val_ds.take(1):
    print(batch)

``` ({'item_ids': , 'padding_mask': }, {'positive_sequence': , 'negative_sequence': }, ) ({'item_ids': , 'padding_mask': }, {'positive_sequence': , 'negative_sequence': }, )

</div>
---
## Model

To encode the input sequence, we use a Transformer decoder-based model. This
part of the model is very similar to the GPT-2 architecture. Refer to the
[GPT text generation from scratch with KerasHub](/examples/generative/text_generation_gpt/#build-the-model)
guide for more details on this part.

One part to note is that when we are "predicting", i.e., `training` is `False`,
we get the embedding corresponding to the last movie in the sequence. This makes
sense, because at inference time, we want to predict the movie the user will
likely watch after watching the last movie.

Also, it's worth discussing the `compute_loss` method. We embed the positive
and negative sequences using the input embedding matrix. We compute the
similarity of (positive sequence, input sequence) and (negative sequence,
input sequence) pair embeddings by computing the dot product. The goal now is
to maximize the similarity of the former and minimize the similarity of
the latter. Let's see this mathematically. Binary Cross Entropy is written
as follows:

loss = - (y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred))

Here, we assign the positive pairs a label of 1 and the negative pairs a label
of 0. So, for a positive pair, the loss reduces to:

loss = -np.log(positive_logits)

Minimising the loss means we want to maximize the log term, which in turn,
implies maximising `positive_logits`. Similarly, we want to minimize
`negative_logits`.


```python

class SasRec(keras.Model):
    def __init__(
        self,
        vocabulary_size,
        num_layers,
        num_heads,
        hidden_dim,
        dropout=0.0,
        max_sequence_length=100,
        dtype=None,
        **kwargs,
    ):
        super().__init__(dtype=dtype, **kwargs)

        # ======== Layers ========

        # === Embeddings ===
        self.item_embedding = keras_hub.layers.ReversibleEmbedding(
            input_dim=vocabulary_size,
            output_dim=hidden_dim,
            embeddings_initializer="glorot_uniform",
            embeddings_regularizer=keras.regularizers.l2(0.001),
            dtype=dtype,
            name="item_embedding",
        )
        self.position_embedding = keras_hub.layers.PositionEmbedding(
            initializer="glorot_uniform",
            sequence_length=max_sequence_length,
            dtype=dtype,
            name="position_embedding",
        )
        self.embeddings_add = keras.layers.Add(
            dtype=dtype,
            name="embeddings_add",
        )
        self.embeddings_dropout = keras.layers.Dropout(
            dropout,
            dtype=dtype,
            name="embeddings_dropout",
        )

        # === Decoder layers ===
        self.transformer_layers = []
        for i in range(num_layers):
            self.transformer_layers.append(
                keras_hub.layers.TransformerDecoder(
                    intermediate_dim=hidden_dim,
                    num_heads=num_heads,
                    dropout=dropout,
                    layer_norm_epsilon=1e-05,
                    # SASRec uses ReLU, although GeLU might be a better option
                    activation="relu",
                    kernel_initializer="glorot_uniform",
                    normalize_first=True,
                    dtype=dtype,
                    name=f"transformer_layer_{i}",
                )
            )

        # === Final layer norm ===
        self.layer_norm = keras.layers.LayerNormalization(
            axis=-1,
            epsilon=1e-8,
            dtype=dtype,
            name="layer_norm",
        )

        # === Retrieval ===
        # The layer that performs the retrieval.
        self.retrieval = keras_rs.layers.BruteForceRetrieval(k=10, return_scores=False)

        # === Loss ===
        self.loss_fn = keras.losses.BinaryCrossentropy(from_logits=True, reduction=None)

        # === Attributes ===
        self.vocabulary_size = vocabulary_size
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.hidden_dim = hidden_dim
        self.dropout = dropout
        self.max_sequence_length = max_sequence_length

    def _get_last_non_padding_token(self, tensor, padding_mask):
        valid_token_mask = ops.logical_not(padding_mask)
        seq_lengths = ops.sum(ops.cast(valid_token_mask, "int32"), axis=1)
        last_token_indices = ops.maximum(seq_lengths - 1, 0)

        indices = ops.expand_dims(last_token_indices, axis=(-2, -1))
        gathered_tokens = ops.take_along_axis(tensor, indices, axis=1)
        last_token_embedding = ops.squeeze(gathered_tokens, axis=1)

        return last_token_embedding

    def build(self, input_shape):
        embedding_shape = list(input_shape) + [self.hidden_dim]

        # Model
        self.item_embedding.build(input_shape)
        self.position_embedding.build(embedding_shape)

        self.embeddings_add.build((embedding_shape, embedding_shape))
        self.embeddings_dropout.build(embedding_shape)

        for transformer_layer in self.transformer_layers:
            transformer_layer.build(decoder_sequence_shape=embedding_shape)

        self.layer_norm.build(embedding_shape)

        # Retrieval
        self.retrieval.candidate_embeddings = self.item_embedding.embeddings
        self.retrieval.build(input_shape)

        # Chain to super
        super().build(input_shape)

    def call(self, inputs, training=False):
        item_ids, padding_mask = inputs["item_ids"], inputs["padding_mask"]

        x = self.item_embedding(item_ids)
        position_embedding = self.position_embedding(x)
        x = self.embeddings_add((x, position_embedding))
        x = self.embeddings_dropout(x)

        for transformer_layer in self.transformer_layers:
            x = transformer_layer(x, decoder_padding_mask=padding_mask)

        item_sequence_embedding = self.layer_norm(x)
        result = {"item_sequence_embedding": item_sequence_embedding}

        # At inference, perform top-k retrieval.
        if not training:
            # need to extract last non-padding token.
            last_item_embedding = self._get_last_non_padding_token(
                item_sequence_embedding, padding_mask
            )
            result["predictions"] = self.retrieval(last_item_embedding)

        return result

    def compute_loss(self, x, y, y_pred, sample_weight, training=False):
        item_sequence_embedding = y_pred["item_sequence_embedding"]
        y_positive_sequence = y["positive_sequence"]
        y_negative_sequence = y["negative_sequence"]

        # Embed positive, negative sequences.
        positive_sequence_embedding = self.item_embedding(y_positive_sequence)
        negative_sequence_embedding = self.item_embedding(y_negative_sequence)

        # Logits
        positive_logits = ops.sum(
            ops.multiply(positive_sequence_embedding, item_sequence_embedding),
            axis=-1,
        )
        negative_logits = ops.sum(
            ops.multiply(negative_sequence_embedding, item_sequence_embedding),
            axis=-1,
        )
        logits = ops.concatenate([positive_logits, negative_logits], axis=1)

        # Labels
        labels = ops.concatenate(
            [
                ops.ones_like(positive_logits),
                ops.zeros_like(negative_logits),
            ],
            axis=1,
        )

        # sample weights
        sample_weight = ops.concatenate(
            [sample_weight, sample_weight],
            axis=1,
        )

        loss = self.loss_fn(
            y_true=ops.expand_dims(labels, axis=-1),
            y_pred=ops.expand_dims(logits, axis=-1),
            sample_weight=sample_weight,
        )
        loss = ops.divide_no_nan(ops.sum(loss), ops.sum(sample_weight))

        return loss

    def compute_output_shape(self, inputs_shape):
        return list(inputs_shape) + [self.hidden_dim]

Let's instantiate our model and do some sanity checks.

model = SasRec(
    vocabulary_size=movies_count + 1,
    num_layers=NUM_LAYERS,
    num_heads=NUM_HEADS,
    hidden_dim=HIDDEN_DIM,
    dropout=DROPOUT,
    max_sequence_length=MAX_CONTEXT_LENGTH,
)

# Training
output = model(
    inputs={
        "item_ids": ops.ones((2, MAX_CONTEXT_LENGTH), dtype="int32"),
        "padding_mask": ops.ones((2, MAX_CONTEXT_LENGTH), dtype="bool"),
    },
    training=True,
)
print(output["item_sequence_embedding"].shape)

# Inference
output = model(
    inputs={
        "item_ids": ops.ones((2, MAX_CONTEXT_LENGTH), dtype="int32"),
        "padding_mask": ops.ones((2, MAX_CONTEXT_LENGTH), dtype="bool"),
    },
    training=False,
)
print(output["predictions"].shape)

``` (2, 200, 50)

(2, 10)

</div>
Now, let's compile and train our model.


```python
model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=LEARNING_RATE, beta_2=0.98),
)
model.fit(
    x=train_ds,
    validation_data=val_ds,
    epochs=NUM_EPOCHS,
)

``` Epoch 1/10

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 13/48 ━━━━━[37m━━━━━━━━━━━━━━━  8s 249ms/step - loss: 0.6697 

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 14/48 ━━━━━[37m━━━━━━━━━━━━━━━  7s 234ms/step - loss: 0.6674

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 15/48 ━━━━━━[37m━━━━━━━━━━━━━━  7s 220ms/step - loss: 0.6651

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 16/48 ━━━━━━[37m━━━━━━━━━━━━━━  6s 208ms/step - loss: 0.6564

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 17/48 ━━━━━━━[37m━━━━━━━━━━━━━  6s 195ms/step - loss: 0.6602
 18/48 ━━━━━━━[37m━━━━━━━━━━━━━  5s 183ms/step - loss: 0.6580

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 19/48 ━━━━━━━[37m━━━━━━━━━━━━━  5s 176ms/step - loss: 0.6508

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 24/48 ━━━━━━━━━━[37m━━━━━━━━━━  3s 139ms/step - loss: 0.6394
 22/48 ━━━━━━━━━[37m━━━━━━━━━━━  3s 153ms/step - loss: 0.6426
 20/48 ━━━━━━━━[37m━━━━━━━━━━━━  4s 167ms/step - loss: 0.6457

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 25/48 ━━━━━━━━━━[37m━━━━━━━━━━  3s 134ms/step - loss: 0.6379

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 23/48 ━━━━━━━━━[37m━━━━━━━━━━━  3s 146ms/step - loss: 0.6410

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 21/48 ━━━━━━━━[37m━━━━━━━━━━━━  4s 158ms/step - loss: 0.6472

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 26/48 ━━━━━━━━━━[37m━━━━━━━━━━  2s 130ms/step - loss: 0.6363

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 29/48 ━━━━━━━━━━━━[37m━━━━━━━━  2s 121ms/step - loss: 0.6242
 30/48 ━━━━━━━━━━━━[37m━━━━━━━━  2s 118ms/step - loss: 0.6229

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 27/48 ━━━━━━━━━━━[37m━━━━━━━━━  2s 128ms/step - loss: 0.6315
 28/48 ━━━━━━━━━━━[37m━━━━━━━━━  2s 124ms/step - loss: 0.6329
 33/48 ━━━━━━━━━━━━━[37m━━━━━━━  1s 108ms/step - loss: 0.6138
 32/48 ━━━━━━━━━━━━━[37m━━━━━━━  1s 111ms/step - loss: 0.6109
 31/48 ━━━━━━━━━━━━[37m━━━━━━━━  1s 113ms/step - loss: 0.6254
 35/48 ━━━━━━━━━━━━━━[37m━━━━━━  1s 101ms/step - loss: 0.6118
 38/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 93ms/step - loss: 0.6055 
 34/48 ━━━━━━━━━━━━━━[37m━━━━━━  1s 105ms/step - loss: 0.6092
 37/48 ━━━━━━━━━━━━━━━[37m━━━━━  1s 96ms/step - loss: 0.6064 

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 36/48 ━━━━━━━━━━━━━━━[37m━━━━━  1s 98ms/step - loss: 0.6148

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 41/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 89ms/step - loss: 0.6028 
 40/48 ━━━━━━━━━━━━━━━━[37m━━━━  0s 91ms/step - loss: 0.6037

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 39/48 ━━━━━━━━━━━━━━━━[37m━━━━  0s 93ms/step - loss: 0.6046
 45/48 ━━━━━━━━━━━━━━━━━━[37m━━  0s 81ms/step - loss: 0.5972

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 47/48 ━━━━━━━━━━━━━━━━━━━[37m━  0s 78ms/step - loss: 0.5923
 43/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 85ms/step - loss: 0.6009

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 42/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 87ms/step - loss: 0.5964
 44/48 ━━━━━━━━━━━━━━━━━━[37m━━  0s 83ms/step - loss: 0.6000

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 46/48 ━━━━━━━━━━━━━━━━━━━[37m━  0s 79ms/step - loss: 0.5981

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 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 138ms/step - loss: 0.5915

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 48/48 ━━━━━━━━━━━━━━━━━━━━ 13s 199ms/step - loss: 0.5908 - val_loss: 0.5149


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Epoch 2/10

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  1/48 [37m━━━━━━━━━━━━━━━━━━━━  2:12 3s/step - loss: 0.4476

    
  2/48 [37m━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4472

    
  3/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4469

    
  4/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4458

    
  5/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4456
  6/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4456

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  7/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4459

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  9/48 ━━━[37m━━━━━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4463
  8/48 ━━━[37m━━━━━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4461

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 10/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4465

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 11/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4467

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 12/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4469

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 13/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 19ms/step - loss: 0.4469

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 14/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 24ms/step - loss: 0.4471

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 15/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 24ms/step - loss: 0.4472

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 16/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 22ms/step - loss: 0.4472

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 20/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 19ms/step - loss: 0.4470
 18/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 21ms/step - loss: 0.4470

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 21/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 18ms/step - loss: 0.4470
 19/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 20ms/step - loss: 0.4472

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 17/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 22ms/step - loss: 0.4472

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 23/48 ━━━━━━━━━[37m━━━━━━━━━━━  0s 20ms/step - loss: 0.4466
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 24/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 21ms/step - loss: 0.4464
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 29/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 22ms/step - loss: 0.4453
 28/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 22ms/step - loss: 0.4452
 33/48 ━━━━━━━━━━━━━[37m━━━━━━━  0s 19ms/step - loss: 0.4454
 27/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 23ms/step - loss: 0.4461
 32/48 ━━━━━━━━━━━━━[37m━━━━━━━  0s 19ms/step - loss: 0.4456
 26/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 23ms/step - loss: 0.4462

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 31/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 20ms/step - loss: 0.4455
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 34/48 ━━━━━━━━━━━━━━[37m━━━━━━  0s 20ms/step - loss: 0.4449

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 36/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 19ms/step - loss: 0.4448

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 37/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 19ms/step - loss: 0.4448
 35/48 ━━━━━━━━━━━━━━[37m━━━━━━  0s 20ms/step - loss: 0.4448

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 40/48 ━━━━━━━━━━━━━━━━[37m━━━━  0s 17ms/step - loss: 0.4446
 45/48 ━━━━━━━━━━━━━━━━━━[37m━━  0s 15ms/step - loss: 0.4443

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 38/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 18ms/step - loss: 0.4446
 44/48 ━━━━━━━━━━━━━━━━━━[37m━━  0s 16ms/step - loss: 0.4442

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 47/48 ━━━━━━━━━━━━━━━━━━━[37m━  0s 15ms/step - loss: 0.4441
 42/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 17ms/step - loss: 0.4445
 41/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 17ms/step - loss: 0.4445

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 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - loss: 0.4441
 46/48 ━━━━━━━━━━━━━━━━━━━[37m━  0s 15ms/step - loss: 0.4442

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 48/48 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - loss: 0.4441 - val_loss: 0.5084


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Epoch 3/10

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  1/48 [37m━━━━━━━━━━━━━━━━━━━━  1s 42ms/step - loss: 0.4316

    
  2/48 [37m━━━━━━━━━━━━━━━━━━━━  0s 11ms/step - loss: 0.4313

    
  3/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4309

    
  4/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4299

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  6/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4298

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  7/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4302

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  8/48 ━━━[37m━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4304

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  9/48 ━━━[37m━━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.4307

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 10/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.4310

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 11/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.4313
 12/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.4316

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 13/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.4317

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 14/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.4319

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 15/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 15ms/step - loss: 0.4321

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 16/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 15ms/step - loss: 0.4322

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 17/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 17ms/step - loss: 0.4323

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 18/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 17ms/step - loss: 0.4323

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 19/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 16ms/step - loss: 0.4324

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 21/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 16ms/step - loss: 0.4324

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 20/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 17ms/step - loss: 0.4324

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 23/48 ━━━━━━━━━[37m━━━━━━━━━━━  0s 16ms/step - loss: 0.4322
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 25/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 15ms/step - loss: 0.4321

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 24/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 16ms/step - loss: 0.4322

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 26/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 16ms/step - loss: 0.4317

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 27/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 16ms/step - loss: 0.4317

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 28/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 17ms/step - loss: 0.4316

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 29/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 19ms/step - loss: 0.4315
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 32/48 ━━━━━━━━━━━━━[37m━━━━━━━  0s 18ms/step - loss: 0.4313
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 33/48 ━━━━━━━━━━━━━[37m━━━━━━━  0s 17ms/step - loss: 0.4313
 39/48 ━━━━━━━━━━━━━━━━[37m━━━━  0s 16ms/step - loss: 0.4311
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 37/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 17ms/step - loss: 0.4311

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 41/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 15ms/step - loss: 0.4310
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 42/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 15ms/step - loss: 0.4311
 46/48 ━━━━━━━━━━━━━━━━━━━[37m━  0s 13ms/step - loss: 0.4309
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 47/48 ━━━━━━━━━━━━━━━━━━━[37m━  0s 13ms/step - loss: 0.4309

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 48/48 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - loss: 0.4308 - val_loss: 0.4923


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Epoch 4/10

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  1/48 [37m━━━━━━━━━━━━━━━━━━━━  1s 41ms/step - loss: 0.4203

    
  2/48 [37m━━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4200

    
  3/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4195

    
  4/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4183

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 10/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.4190

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 12/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.4195
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 13/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 14ms/step - loss: 0.4196
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 15/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 14ms/step - loss: 0.4199

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 16/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 16ms/step - loss: 0.4199

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 17/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 19ms/step - loss: 0.4200

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 19/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 17ms/step - loss: 0.4200

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 22/48 ━━━━━━━━━[37m━━━━━━━━━━━  0s 15ms/step - loss: 0.4198

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 21/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 16ms/step - loss: 0.4198
 18/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 18ms/step - loss: 0.4199
 20/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 16ms/step - loss: 0.4200

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 23/48 ━━━━━━━━━[37m━━━━━━━━━━━  0s 17ms/step - loss: 0.4197

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 24/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 16ms/step - loss: 0.4196

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 25/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 18ms/step - loss: 0.4195
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 26/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 18ms/step - loss: 0.4193

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 28/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 17ms/step - loss: 0.4192

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 29/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 18ms/step - loss: 0.4191
 30/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 20ms/step - loss: 0.4189

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 33/48 ━━━━━━━━━━━━━[37m━━━━━━━  0s 19ms/step - loss: 0.4188
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 32/48 ━━━━━━━━━━━━━[37m━━━━━━━  0s 19ms/step - loss: 0.4187
 31/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 19ms/step - loss: 0.4190

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 37/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 17ms/step - loss: 0.4186
 42/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 15ms/step - loss: 0.4185
 35/48 ━━━━━━━━━━━━━━[37m━━━━━━  0s 18ms/step - loss: 0.4186

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 41/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 15ms/step - loss: 0.4185

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 48/48 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - loss: 0.4182 - val_loss: 0.4797


<div class="k-default-codeblock">

Epoch 5/10

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  1/48 [37m━━━━━━━━━━━━━━━━━━━━  1s 41ms/step - loss: 0.4058

    
  2/48 [37m━━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4057

    
  3/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4053

    
  4/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4041

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  6/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4036

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  7/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4037

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  8/48 ━━━[37m━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.4038

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 10/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.4042

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 11/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.4044

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 12/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.4045

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 13/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 14ms/step - loss: 0.4045
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 15/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 14ms/step - loss: 0.4046

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 16/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 15ms/step - loss: 0.4046

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 17/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 16ms/step - loss: 0.4046

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 18/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 17ms/step - loss: 0.4046

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 19/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 17ms/step - loss: 0.4045

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 20/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 16ms/step - loss: 0.4045

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 21/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 17ms/step - loss: 0.4044
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 23/48 ━━━━━━━━━[37m━━━━━━━━━━━  0s 16ms/step - loss: 0.4041
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 25/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 16ms/step - loss: 0.4037

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 26/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 17ms/step - loss: 0.4036
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 28/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 16ms/step - loss: 0.4033

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 29/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 17ms/step - loss: 0.4032
 30/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 18ms/step - loss: 0.4031
 32/48 ━━━━━━━━━━━━━[37m━━━━━━━  0s 18ms/step - loss: 0.4029

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 34/48 ━━━━━━━━━━━━━━[37m━━━━━━  0s 18ms/step - loss: 0.4027
 31/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 18ms/step - loss: 0.4030

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 37/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 17ms/step - loss: 0.4025
 36/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 17ms/step - loss: 0.4023

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 38/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 16ms/step - loss: 0.4022
 40/48 ━━━━━━━━━━━━━━━━[37m━━━━  0s 15ms/step - loss: 0.4023
 39/48 ━━━━━━━━━━━━━━━━[37m━━━━  0s 16ms/step - loss: 0.4024

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 41/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 15ms/step - loss: 0.4020
 33/48 ━━━━━━━━━━━━━[37m━━━━━━━  0s 19ms/step - loss: 0.4028

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 35/48 ━━━━━━━━━━━━━━[37m━━━━━━  0s 18ms/step - loss: 0.4026

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 44/48 ━━━━━━━━━━━━━━━━━━[37m━━  0s 14ms/step - loss: 0.4019
 42/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 15ms/step - loss: 0.4020

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 48/48 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - loss: 0.4014 - val_loss: 0.4611


<div class="k-default-codeblock">

Epoch 6/10

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  1/48 [37m━━━━━━━━━━━━━━━━━━━━  1s 41ms/step - loss: 0.3831

    
  2/48 [37m━━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3830

    
  3/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3827

    
  4/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3816

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  6/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3811

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  7/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3811

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  8/48 ━━━[37m━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3811

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  9/48 ━━━[37m━━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3812

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 10/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3813

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 11/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3814

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 12/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3815

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 17/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 17ms/step - loss: 0.3811

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 19/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 17ms/step - loss: 0.3809
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 18/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 18ms/step - loss: 0.3809

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 23/48 ━━━━━━━━━[37m━━━━━━━━━━━  0s 17ms/step - loss: 0.3802
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 22/48 ━━━━━━━━━[37m━━━━━━━━━━━  0s 17ms/step - loss: 0.3804
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 26/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 17ms/step - loss: 0.3795

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 27/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 18ms/step - loss: 0.3793

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 28/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 18ms/step - loss: 0.3792
 33/48 ━━━━━━━━━━━━━[37m━━━━━━━  0s 18ms/step - loss: 0.3786
 29/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 18ms/step - loss: 0.3791
 32/48 ━━━━━━━━━━━━━[37m━━━━━━━  0s 19ms/step - loss: 0.3785
 31/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 20ms/step - loss: 0.3788
 30/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 20ms/step - loss: 0.3789

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 34/48 ━━━━━━━━━━━━━━[37m━━━━━━  0s 18ms/step - loss: 0.3783
 39/48 ━━━━━━━━━━━━━━━━[37m━━━━  0s 16ms/step - loss: 0.3778
 38/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 16ms/step - loss: 0.3775
 37/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 17ms/step - loss: 0.3778

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 36/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 17ms/step - loss: 0.3776
 35/48 ━━━━━━━━━━━━━━[37m━━━━━━  0s 18ms/step - loss: 0.3777

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 48/48 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - loss: 0.3760 - val_loss: 0.4355


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Epoch 7/10

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  1/48 [37m━━━━━━━━━━━━━━━━━━━━  1s 41ms/step - loss: 0.3559

    
  2/48 [37m━━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3559

    
  3/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3555

    
  4/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3544

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  8/48 ━━━[37m━━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3538
  7/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3539

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 10/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3539

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 11/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3540

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 12/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3540

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 13/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 14ms/step - loss: 0.3540
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 15/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 14ms/step - loss: 0.3539

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 16/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 16ms/step - loss: 0.3538

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 17/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 19ms/step - loss: 0.3537
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 20/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 17ms/step - loss: 0.3534

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 19/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 18ms/step - loss: 0.3533
 21/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 16ms/step - loss: 0.3531

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 26/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 15ms/step - loss: 0.3524

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 22/48 ━━━━━━━━━[37m━━━━━━━━━━━  0s 18ms/step - loss: 0.3529

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 25/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 16ms/step - loss: 0.3525
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 23/48 ━━━━━━━━━[37m━━━━━━━━━━━  0s 17ms/step - loss: 0.3523

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 27/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 17ms/step - loss: 0.3519

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 29/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 18ms/step - loss: 0.3517

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 32/48 ━━━━━━━━━━━━━[37m━━━━━━━  0s 18ms/step - loss: 0.3513
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 31/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 19ms/step - loss: 0.3513
 30/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 18ms/step - loss: 0.3514
 28/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 19ms/step - loss: 0.3516

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 34/48 ━━━━━━━━━━━━━━[37m━━━━━━  0s 18ms/step - loss: 0.3511
 37/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 17ms/step - loss: 0.3508
 36/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 17ms/step - loss: 0.3510

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 35/48 ━━━━━━━━━━━━━━[37m━━━━━━  0s 18ms/step - loss: 0.3509
 39/48 ━━━━━━━━━━━━━━━━[37m━━━━  0s 16ms/step - loss: 0.3508
 38/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 16ms/step - loss: 0.3508

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 40/48 ━━━━━━━━━━━━━━━━[37m━━━━  0s 16ms/step - loss: 0.3507

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 48/48 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - loss: 0.3500 - val_loss: 0.4174


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Epoch 8/10

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  1/48 [37m━━━━━━━━━━━━━━━━━━━━  1s 41ms/step - loss: 0.3339

    
  2/48 [37m━━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3343

    
  3/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3341

    
  4/48 ━[37m━━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3333

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  6/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 12ms/step - loss: 0.3329

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  7/48 ━━[37m━━━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3329

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  8/48 ━━━[37m━━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3328

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  9/48 ━━━[37m━━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3328

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 10/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3329

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 11/48 ━━━━[37m━━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3330

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 12/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3330

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 13/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3330

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 14/48 ━━━━━[37m━━━━━━━━━━━━━━━  0s 13ms/step - loss: 0.3330

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 15/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 14ms/step - loss: 0.3329

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 16/48 ━━━━━━[37m━━━━━━━━━━━━━━  0s 15ms/step - loss: 0.3328

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 17/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 17ms/step - loss: 0.3327

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 18/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 17ms/step - loss: 0.3326

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 19/48 ━━━━━━━[37m━━━━━━━━━━━━━  0s 18ms/step - loss: 0.3323
 21/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 16ms/step - loss: 0.3324

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 20/48 ━━━━━━━━[37m━━━━━━━━━━━━  0s 17ms/step - loss: 0.3325

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 24/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 16ms/step - loss: 0.3318

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 23/48 ━━━━━━━━━[37m━━━━━━━━━━━  0s 17ms/step - loss: 0.3319
 22/48 ━━━━━━━━━[37m━━━━━━━━━━━  0s 17ms/step - loss: 0.3320

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 26/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 16ms/step - loss: 0.3317

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 25/48 ━━━━━━━━━━[37m━━━━━━━━━━  0s 17ms/step - loss: 0.3318

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 27/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 17ms/step - loss: 0.3316

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 28/48 ━━━━━━━━━━━[37m━━━━━━━━━  0s 17ms/step - loss: 0.3315

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 30/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 18ms/step - loss: 0.3314
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 31/48 ━━━━━━━━━━━━[37m━━━━━━━━  0s 18ms/step - loss: 0.3312

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 39/48 ━━━━━━━━━━━━━━━━[37m━━━━  0s 16ms/step - loss: 0.3309
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 34/48 ━━━━━━━━━━━━━━[37m━━━━━━  0s 18ms/step - loss: 0.3310

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 38/48 ━━━━━━━━━━━━━━━[37m━━━━━  0s 16ms/step - loss: 0.3308
 41/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 15ms/step - loss: 0.3307

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 44/48 ━━━━━━━━━━━━━━━━━━[37m━━  0s 14ms/step - loss: 0.3307

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 42/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 15ms/step - loss: 0.3307
 43/48 ━━━━━━━━━━━━━━━━━[37m━━━  0s 15ms/step - loss: 0.3307

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Epoch 10/10

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<keras.src.callbacks.history.History at 0x7e0816f6b4d0>

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---
## Making predictions

Now that we have a model, we would like to be able to make predictions.

So far, we have only handled movies by id. Now is the time to create a mapping
keyed by movie IDs to be able to surface the titles.


```python
movie_id_to_movie_title = dict(zip(movies_df["MovieID"], movies_df["Title"]))
movie_id_to_movie_title[0] = ""  # Because id 0 is not in the dataset.

We then simply use the Keras model.predict() method. Under the hood, it calls the BruteForceRetrieval layer to perform the actual retrieval.

Note that this model can retrieve movies already watched by the user. We could easily add logic to remove them if that is desirable.

for ele in val_ds.unbatch().take(1):
    test_sample = ele[0]
    test_sample["item_ids"] = tf.expand_dims(test_sample["item_ids"], axis=0)
    test_sample["padding_mask"] = tf.expand_dims(test_sample["padding_mask"], axis=0)

movie_sequence = np.array(test_sample["item_ids"])[0]
for movie_id in movie_sequence:
    if movie_id == 0:
        continue
    print(movie_id_to_movie_title[movie_id], end="; ")
print()

predictions = model.predict(test_sample)["predictions"]
predictions = keras.ops.convert_to_numpy(predictions)

for movie_id in predictions[0]:
    print(movie_id_to_movie_title[movie_id])

``` Girl, Interrupted (1999); Back to the Future (1985); Titanic (1997); Cinderella (1950); Meet Joe Black (1998); Last Days of Disco, The (1998); Erin Brockovich (2000); Christmas Story, A (1983); To Kill a Mockingbird (1962); One Flew Over the Cuckoo's Nest (1975); Wallace & Gromit: The Best of Aardman Animation (1996); Star Wars: Episode IV - A New Hope (1977); Wizard of Oz, The (1939); Fargo (1996); Run Lola Run (Lola rennt) (1998); Rain Man (1988); Saving Private Ryan (1998); Awakenings (1990); Gigi (1958); Sound of Music, The (1965); Driving Miss Daisy (1989); Bambi (1942); Apollo 13 (1995); Mary Poppins (1964); E.T. the Extra-Terrestrial (1982); My Fair Lady (1964); Ben-Hur (1959); Big (1988); Sixth Sense, The (1999); Dead Poets Society (1989); James and the Giant Peach (1996); Ferris Bueller's Day Off (1986); Secret Garden, The (1993); Toy Story 2 (1999); Airplane! (1980); Pleasantville (1998); Dumbo (1941); Princess Bride, The (1987); Snow White and the Seven Dwarfs (1937); Miracle on 34th Street (1947); Ponette (1996); Schindler's List (1993); Beauty and the Beast (1991); Tarzan (1999); Close Shave, A (1995); Aladdin (1992); Toy Story (1995); Bug's Life, A (1998); Antz (1998); Hunchback of Notre Dame, The (1996); Hercules (1997); Mulan (1998); Pocahontas (1995);

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 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 791ms/step


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Groundhog Day (1993) Aladdin (1992) Toy Story (1995) Forrest Gump (1994) Bug's Life, A (1998) Lion King, The (1994) Shakespeare in Love (1998) American Beauty (1999) Sixth Sense, The (1999) Ghostbusters (1984)

</div>
And that's all!

Sequential retrieval using SASRec

Introduction

Dataset

Product

Resources

Company