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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Timeseries classification with a Transformer model
Author
Step1:  Build the model
Our model processes a tensor of shape (batch size, sequence length, features),
where sequence length is the number of time steps and features is each input
timeseries.
You can replace your classification RNN layers with this one
Step2:  We include residual connections, layer normalization, and dropout.
The resulting layer can be stacked multiple times.
The projection layers are implemented through keras.layers.Conv1D.
Step3:  The main part of our model is now complete. We can stack multiple of those
transformer_encoder blocks and we can also proceed to add the final
Multi-Layer Perceptron classification head. Apart from a stack of Dense
layers, we need to reduce the output tensor of the TransformerEncoder part of
our model down to a vector of features for each data point in the current
batch. A common way to achieve this is to use a pooling layer. For
this example, a GlobalAveragePooling1D layer is sufficient.
Step4:  Train and evaluate
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Python Code:
import numpy as np
def readucr(filename):
    data = np.loadtxt(filename, delimiter="\t")
    y = data[:, 0]
    x = data[:, 1:]
    return x, y.astype(int)
root_url = "https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/"
x_train, y_train = readucr(root_url + "FordA_TRAIN.tsv")
x_test, y_test = readucr(root_url + "FordA_TEST.tsv")
x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], 1))
x_test = x_test.reshape((x_test.shape[0], x_test.shape[1], 1))
n_classes = len(np.unique(y_train))
idx = np.random.permutation(len(x_train))
x_train = x_train[idx]
y_train = y_train[idx]
y_train[y_train == -1] = 0
y_test[y_test == -1] = 0
Explanation: Timeseries classification with a Transformer model
Author: Theodoros Ntakouris<br>
Date created: 2021/06/25<br>
Last modified: 2021/08/05<br>
Description: This notebook demonstrates how to do timeseries classification using a Transformer model.
Introduction
This is the Transformer architecture from
Attention Is All You Need,
applied to timeseries instead of natural language.
This example requires TensorFlow 2.4 or higher.
Load the dataset
We are going to use the same dataset and preprocessing as the
TimeSeries Classification from Scratch
example.
End of explanation
from tensorflow import keras
from tensorflow.keras import layers
Explanation: Build the model
Our model processes a tensor of shape (batch size, sequence length, features),
where sequence length is the number of time steps and features is each input
timeseries.
You can replace your classification RNN layers with this one: the
inputs are fully compatible!
End of explanation
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
Explanation: We include residual connections, layer normalization, and dropout.
The resulting layer can be stacked multiple times.
The projection layers are implemented through keras.layers.Conv1D.
End of explanation
def build_model(
    input_shape,
    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)
Explanation: The main part of our model is now complete. We can stack multiple of those
transformer_encoder blocks and we can also proceed to add the final
Multi-Layer Perceptron classification head. Apart from a stack of Dense
layers, we need to reduce the output tensor of the TransformerEncoder part of
our model down to a vector of features for each data point in the current
batch. A common way to achieve this is to use a pooling layer. For
this example, a GlobalAveragePooling1D layer is sufficient.
End of explanation
input_shape = x_train.shape[1:]
model = build_model(
    input_shape,
    head_size=256,
    num_heads=4,
    ff_dim=4,
    num_transformer_blocks=4,
    mlp_units=[128],
    mlp_dropout=0.4,
    dropout=0.25,
)
model.compile(
    loss="sparse_categorical_crossentropy",
    optimizer=keras.optimizers.Adam(learning_rate=1e-4),
    metrics=["sparse_categorical_accuracy"],
)
model.summary()
callbacks = [keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True)]
model.fit(
    x_train,
    y_train,
    validation_split=0.2,
    epochs=200,
    batch_size=64,
    callbacks=callbacks,
)
model.evaluate(x_test, y_test, verbose=1)
Explanation: Train and evaluate
End of explanation
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