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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:
 Copyright 2020 The TensorFlow Authors.
Step1:  Text generation with an RNN
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  <td>
    <a target="_blank" href="https
Step2:  Download the Shakespeare dataset
Change the following line to run this code on your own data.
Step3:  Read the data
First, look in the text
Step4:  Process the text
Vectorize the text
Before training, we need to map strings to a numerical representation. Create two lookup tables
Step5:  The prediction task
Given a character, or a sequence of characters, what is the most probable next character? This is the task we're training the model to perform. The input to the model will be a sequence of characters, and we train the model to predict the output—the following character at each time step.
Since RNNs maintain an internal state that depends on the previously seen elements, given all the characters computed until this moment, what is the next character?
Create training examples and targets
Next divide the text into example sequences. Each input sequence will contain seq_length characters from the text.
For each input sequence, the corresponding targets contain the same length of text, except shifted one character to the right.
So break the text into chunks of seq_length+1. For example, say seq_length is 4 and our text is "Hello". The input sequence would be "Hell", and the target sequence "ello".
Step6:  Each index of these vectors are processed as one time step. For the input at time step 0, the model receives the index for "F" and tries to predict the index for "i" as the next character. At the next timestep, it does the same thing but the RNN considers the previous step context in addition to the current input character.
Step7:  Create training batches
We used tf.data to split the text into manageable sequences. But before feeding this data into the model, we need to shuffle the data and pack it into batches.
Step9:  Build The Model
We manually implement the model from scratch, using tf.numpy and some low-level TF ops. A Model object has three layers
Step10:  For each character the model looks up the embedding, runs the GRU one timestep with the embedding as input, and applies the dense layer to generate logits predicting the log-likelihood of the next character.
Try the model
Now run the model to see that it behaves as expected.
First check the shape of the output
Step11:  In the above example the sequence length of the input is 100 but the model can be run on inputs of any length
Step12:  This gives us, at each timestep, a prediction of the next character index
Step13:  Decode these to see the text predicted by this untrained model
Step14:  Train the model
At this point the problem can be treated as a standard classification problem. Given the previous RNN state, and the input this time step, predict the class of the next character.
Loss function
We define the loss function from scratch, using tf.nn.log_softmax. (Our definition is the same as tf.keras.losses.sparse_categorical_crossentropy.)
Step15:  Optimizer
Keeping the DIY spirit, we implement the Adam optimizer from scratch.
Step16:  Training loop
Again, we write our training loop from scratch.
To keep training time reasonable, use 10 epochs to train the model. In Colab, set the runtime to GPU for faster training.
Step17:  Generate text
The following code block generates the text
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Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
Explanation: Copyright 2020 The TensorFlow Authors.
End of explanation
import tensorflow as tf
import tensorflow.experimental.numpy as tnp
import numpy as np
import os
import time
Explanation: Text generation with an RNN
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    <a target="_blank" href="https://www.tensorflow.org/tutorials/text/text_generation"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
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    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/text/text_generation.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
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    <a target="_blank" href="https://github.com/tensorflow/docs/blob/master/site/en/tutorials/text/text_generation.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
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    <a href="https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/text/text_generation.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
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This tutorial demonstrates how to generate text using a character-based RNN. We will work with a dataset of Shakespeare's writing from Andrej Karpathy's The Unreasonable Effectiveness of Recurrent Neural Networks. Given a sequence of characters from this data ("Shakespear"), train a model to predict the next character in the sequence ("e"). Longer sequences of text can be generated by calling the model repeatedly.
Note: Enable GPU acceleration to execute this notebook faster. In Colab: Runtime > Change runtime type > Hardware accelerator > GPU. If running locally make sure TensorFlow version >= 2.4.
This tutorial includes runnable code implemented using tf.experimental.numpy. The following is sample output when the model in this tutorial trained for 30 epochs, and started with the string "Q":
<pre>
QUEENE:
I had thought thou hadst a Roman; for the oracle,
Thus by All bids the man against the word,
Which are so weak of care, by old care done;
Your children were in your holy love,
And the precipitation through the bleeding throne.
BISHOP OF ELY:
Marry, and will, my lord, to weep in such a one were prettiest;
Yet now I was adopted heir
Of the world's lamentable day,
To watch the next way with his father with his face?
ESCALUS:
The cause why then we are all resolved more sons.
VOLUMNIA:
O, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, it is no sin it should be dead,
And love and pale as any will to that word.
QUEEN ELIZABETH:
But how long have I heard the soul for this world,
And show his hands of life be proved to stand.
PETRUCHIO:
I say he look'd on, if I must be content
To stay him from the fatal of our country's bliss.
His lordship pluck'd from this sentence then for prey,
And then let us twain, being the moon,
were she such a case as fills m
</pre>
While some of the sentences are grammatical, most do not make sense. The model has not learned the meaning of words, but consider:
The model is character-based. When training started, the model did not know how to spell an English word, or that words were even a unit of text.
The structure of the output resembles a play—blocks of text generally begin with a speaker name, in all capital letters similar to the dataset.
As demonstrated below, the model is trained on small batches of text (100 characters each), and is still able to generate a longer sequence of text with coherent structure.
Setup
Import TensorFlow and other libraries
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path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')
Explanation: Download the Shakespeare dataset
Change the following line to run this code on your own data.
End of explanation
# Read, then decode for py2 compat.
text = open(path_to_file, 'rb').read().decode(encoding='utf-8')
# length of text is the number of characters in it
print ('Length of text: {} characters'.format(len(text)))
# Take a look at the first 250 characters in text
print(text[:250])
# The unique characters in the file
vocab = sorted(set(text))
print ('{} unique characters'.format(len(vocab)))
Explanation: Read the data
First, look in the text:
End of explanation
# Creating a mapping from unique characters to indices
char2idx = {u:i for i, u in enumerate(vocab)}
idx2char = np.array(vocab)
text_as_int = np.array([char2idx[c] for c in text])
Explanation: Process the text
Vectorize the text
Before training, we need to map strings to a numerical representation. Create two lookup tables: one mapping characters to numbers, and another for numbers to characters.
End of explanation
# The maximum length sentence we want for a single input in characters
seq_length = 100
examples_per_epoch = len(text)//(seq_length+1)
# Create training examples / targets
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)
for i in char_dataset.take(5):
  print(idx2char[i.numpy()])
sequences = char_dataset.batch(seq_length+1, drop_remainder=True)
for item in sequences.take(5):
  print(repr(''.join(idx2char[item.numpy()])))
def split_input_target(chunk):
    input_text = chunk[:-1]
    target_text = chunk[1:]
    return input_text, target_text
dataset = sequences.map(split_input_target)
for input_example, target_example in  dataset.take(1):
  print ('Input data: ', repr(''.join(idx2char[input_example.numpy()])))
  print ('Target data:', repr(''.join(idx2char[target_example.numpy()])))
Explanation: The prediction task
Given a character, or a sequence of characters, what is the most probable next character? This is the task we're training the model to perform. The input to the model will be a sequence of characters, and we train the model to predict the output—the following character at each time step.
Since RNNs maintain an internal state that depends on the previously seen elements, given all the characters computed until this moment, what is the next character?
Create training examples and targets
Next divide the text into example sequences. Each input sequence will contain seq_length characters from the text.
For each input sequence, the corresponding targets contain the same length of text, except shifted one character to the right.
So break the text into chunks of seq_length+1. For example, say seq_length is 4 and our text is "Hello". The input sequence would be "Hell", and the target sequence "ello".
End of explanation
for i, (input_idx, target_idx) in enumerate(zip(input_example[:5], target_example[:5])):
    print("Step {:4d}".format(i))
    print("  input: {} ({:s})".format(input_idx, repr(idx2char[input_idx])))
    print("  expected output: {} ({:s})".format(target_idx, repr(idx2char[target_idx])))
Explanation: Each index of these vectors are processed as one time step. For the input at time step 0, the model receives the index for "F" and tries to predict the index for "i" as the next character. At the next timestep, it does the same thing but the RNN considers the previous step context in addition to the current input character.
End of explanation
# Batch size
BATCH_SIZE = 64
# Buffer size to shuffle the dataset
# (TF data is designed to work with possibly infinite sequences,
# so it doesn't attempt to shuffle the entire sequence in memory. Instead,
# it maintains a buffer in which it shuffles elements).
BUFFER_SIZE = 10000
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
dataset
Explanation: Create training batches
We used tf.data to split the text into manageable sequences. But before feeding this data into the model, we need to shuffle the data and pack it into batches.
End of explanation
# Length of the vocabulary in chars
vocab_size = len(vocab)
# The embedding dimension
embedding_dim = 256
# Number of RNN units
rnn_units = 1024
class Embedding:
  def __init__(self, vocab_size, embedding_dim):
    self._vocab_size = vocab_size
    self._embedding_dim = embedding_dim
    self._built = False
  def __call__(self, inputs):
    if not self._built:
      self.build(inputs)
    return tnp.take(self.weights, inputs, axis=0)
  def build(self, inputs):
    del inputs
    self.weights = tf.Variable(tnp.random.randn(
        self._vocab_size, self._embedding_dim).astype(np.float32))
    self._built = True
class GRUCell:
  Builds a traditional GRU cell with dense internal transformations.
  Gated Recurrent Unit paper: https://arxiv.org/abs/1412.3555
  
  def __init__(self, n_units, forget_bias=0.0):
    self._n_units = n_units
    self._forget_bias = forget_bias
    self._built = False
  def __call__(self, inputs):
    if not self._built:
      self.build(inputs)
    x, gru_state = inputs
    # Dense layer on the concatenation of x and h.
    y = tnp.dot(tnp.concatenate([x, gru_state], axis=-1), self.w1) + self.b1
    # Update and reset gates.
    u, r = tnp.split(tf.sigmoid(y), 2, axis=-1)
    # Candidate.
    c = tnp.dot(tnp.concatenate([x, r * gru_state], axis=-1), self.w2) + self.b2
    new_gru_state = u * gru_state + (1 - u) * tnp.tanh(c)
    return new_gru_state
  def build(self, inputs):
    # State last dimension must be n_units.
    assert inputs[1].shape[-1] == self._n_units
    # The dense layer input is the input and half of the GRU state.
    dense_shape = inputs[0].shape[-1] + self._n_units
    self.w1 = tf.Variable(tnp.random.uniform(
        -0.01, 0.01, (dense_shape, 2 * self._n_units)).astype(tnp.float32))
    self.b1 = tf.Variable((tnp.random.randn(2 * self._n_units) * 1e-6 + self._forget_bias
               ).astype(tnp.float32))
    self.w2 = tf.Variable(tnp.random.uniform(
        -0.01, 0.01, (dense_shape, self._n_units)).astype(tnp.float32))
    self.b2 = tf.Variable((tnp.random.randn(self._n_units) * 1e-6).astype(tnp.float32))
    self._built = True
  @property
  def weights(self):
    return (self.w1, self.b1, self.w2, self.b2)
class GRU:
  def __init__(self, n_units, forget_bias=0.0, stateful=False):
    self._cell = GRUCell(n_units, forget_bias)
    self._stateful = stateful
    self._built = False
  def __call__(self, inputs):
    if not self._built:
      self.build(inputs)
    if self._stateful:
      state = self.state.read_value()
    else:
      state = self._init_state(inputs.shape[0])    
    inputs = tnp.transpose(inputs, (1, 0, 2))
    output =  tf.scan(
        lambda gru_state, x: self._cell((x, gru_state)),
        inputs, state)
    if self._stateful:
      self.state.assign(output[-1, ...])
    return tnp.transpose(output, [1, 0, 2])
  def _init_state(self, batch_size):
    return tnp.zeros([batch_size, self._cell._n_units], tnp.float32)
  def reset_state(self):
    if not self._stateful:
      return
    self.state.assign(tf.zeros_like(self.state))
  def create_state(self, batch_size):
    self.state = tf.Variable(self._init_state(batch_size))
  def build(self, inputs):
    s = inputs.shape[0:1] + inputs.shape[2:]
    shapes = (s, s[:-1] + (self._cell._n_units,))   
    self._cell.build([tf.TensorSpec(x, tf.float32) for x in shapes])
    if self._stateful:
      self.create_state(inputs.shape[0])
    else:
      self.state = ()
    self._built = True
    
  @property
  def weights(self):
    return self._cell.weights
class Dense:
  def __init__(self, n_units, activation=None):
    self._n_units = n_units
    self._activation = activation
    self._built = False
  def __call__(self, inputs):
    if not self._built:
      self.build(inputs)
    y = tnp.dot(inputs, self.w) +self.b
    if self._activation != None:
      y = self._activation(y)
    return y
  def build(self, inputs):
    shape_w = (inputs.shape[-1], self._n_units)
    lim = tnp.sqrt(6.0 / (shape_w[0] + shape_w[1]))
    self.w = tf.Variable(tnp.random.uniform(-lim, lim, shape_w).astype(tnp.float32))
    self.b = tf.Variable((tnp.random.randn(self._n_units) * 1e-6).astype(tnp.float32))
    self._built = True
  @property
  def weights(self):
    return (self.w, self.b)
class Model:
  def __init__(self, vocab_size, embedding_dim, rnn_units, forget_bias=0.0, stateful=False, activation=None):
    self._embedding = Embedding(vocab_size, embedding_dim)
    self._gru = GRU(rnn_units, forget_bias=forget_bias, stateful=stateful)
    self._dense = Dense(vocab_size, activation=activation)
    self._layers = [self._embedding, self._gru, self._dense]
    self._built = False
  def __call__(self, inputs):
    if not self._built:
      self.build(inputs)
    xs = inputs
    for layer in self._layers:
      xs = layer(xs)
    return xs
    
  def build(self, inputs):
    self._embedding.build(inputs)
    self._gru.build(tf.TensorSpec(inputs.shape + (self._embedding._embedding_dim,), tf.float32))
    self._dense.build(tf.TensorSpec(inputs.shape + (self._gru._cell._n_units,), tf.float32))
    self._built = True
  @property
  def weights(self):
    return [layer.weights for layer in self._layers]
  @property
  def state(self):
    return self._gru.state
  def create_state(self, *args):
    self._gru.create_state(*args)
  def reset_state(self, *args):
    self._gru.reset_state(*args)
model = Model(
  vocab_size = vocab_size,
  embedding_dim=embedding_dim,
  rnn_units=rnn_units,
  stateful=True)
Explanation: Build The Model
We manually implement the model from scratch, using tf.numpy and some low-level TF ops. A Model object has three layers: Embedding, GRU and Dense. Embedding and Dense are little more than just wrappers around tnp.take and tnp.dot, but we can use them to familiarize ourself with the structure of a layer. Each layer has two essential methods: build and __call__. build creates and initializes the layer's weights and state, which are things that change during the training process. __call__ is the forward function that calculates outputs given inputs, using the layer's weights and state internally.
Our model (more precisely the GRU layer) is stateful, because each call of __call__ will change its internal state, affecting the next call.
End of explanation
  for input_example_batch, target_example_batch in dataset.take(1):
    input_example_batch = tnp.asarray(input_example_batch)
    example_batch_predictions = model(input_example_batch)
    print(example_batch_predictions.shape, "# (batch_size, sequence_length, vocab_size)")
Explanation: For each character the model looks up the embedding, runs the GRU one timestep with the embedding as input, and applies the dense layer to generate logits predicting the log-likelihood of the next character.
Try the model
Now run the model to see that it behaves as expected.
First check the shape of the output:
End of explanation
example_batch_predictions[0]
sampled_indices = tf.random.categorical(example_batch_predictions[0], num_samples=1)
sampled_indices = tf.squeeze(sampled_indices,axis=-1).numpy()
Explanation: In the above example the sequence length of the input is 100 but the model can be run on inputs of any length:
To get actual predictions from the model we need to sample from the output distribution, to get actual character indices. This distribution is defined by the logits over the character vocabulary.
Note: It is important to sample from this distribution as taking the argmax of the distribution can easily get the model stuck in a loop.
Try it for the first example in the batch:
End of explanation
sampled_indices
Explanation: This gives us, at each timestep, a prediction of the next character index:
End of explanation
print("Input: \n", repr("".join(idx2char[input_example_batch[0]])))
print()
print("Next Char Predictions: \n", repr("".join(idx2char[sampled_indices ])))
Explanation: Decode these to see the text predicted by this untrained model:
End of explanation
def one_hot(labels, n):
  return (labels[..., np.newaxis] == tnp.arange(n)).astype(np.float32)
def loss_fn(labels, predictions):
  predictions = tf.nn.log_softmax(predictions)
  return -tnp.sum(predictions * one_hot(tnp.asarray(labels), predictions.shape[-1]), axis=-1)
example_batch_loss  = loss_fn(target_example_batch, example_batch_predictions)
print("Prediction shape: ", example_batch_predictions.shape, " # (batch_size, sequence_length, vocab_size)")
print("scalar_loss:      ", tnp.mean(example_batch_loss))
Explanation: Train the model
At this point the problem can be treated as a standard classification problem. Given the previous RNN state, and the input this time step, predict the class of the next character.
Loss function
We define the loss function from scratch, using tf.nn.log_softmax. (Our definition is the same as tf.keras.losses.sparse_categorical_crossentropy.)
End of explanation
class Adam:
  def __init__(self, learning_rate=0.001, b1=0.9, b2=0.999, eps=1e-7):
    self._lr = learning_rate
    self._b1 = b1
    self._b2 = b2
    self._eps = eps
    self._built = False
  def build(self, weights):
    self._m = tf.nest.map_structure(lambda x: tf.Variable(tnp.zeros_like(x)), weights)
    self._v = tf.nest.map_structure(lambda x: tf.Variable(tnp.zeros_like(x)), weights)
    self._step = tf.Variable(tnp.asarray(0, np.int64))
    self._built = True
  def _update(self, weights_var, grads, m_var, v_var):
    b1 = self._b1
    b2 = self._b2
    eps = self._eps
    step = tnp.asarray(self._step, np.float32)
    lr = self._lr
    weights = tnp.asarray(weights_var)
    m = tnp.asarray(m_var)
    v = tnp.asarray(v_var)
    m = (1 - b1) * grads + b1 * m  # First  moment estimate.
    v = (1 - b2) * (grads ** 2) + b2 * v  # Second moment estimate.
    mhat = m / (1 - b1 ** (step + 1))  # Bias correction.
    vhat = v / (1 - b2 ** (step + 1))   
    weights_var.assign_sub((lr * mhat / (tnp.sqrt(vhat) + eps)).astype(weights.dtype))
    m_var.assign(m)
    v_var.assign(v)
  def apply_gradients(self, weights, grads):
    if not self._built:
      self.build(weights)
    tf.nest.map_structure(lambda *args: self._update(*args), weights, grads, self._m, self._v)
    self._step.assign_add(1)
  @property
  def state(self):
    return (self._step, self._m, self._v)
optimizer = Adam()
Explanation: Optimizer
Keeping the DIY spirit, we implement the Adam optimizer from scratch.
End of explanation
@tf.function
def train_step(inp, target):
  with tf.GradientTape() as tape:
    # tape.watch(tf.nest.flatten(weights))
    predictions = model(inp)
    loss = tnp.mean(loss_fn(target, predictions))
  weights = model.weights
  grads = tape.gradient(loss, weights)
  optimizer.apply_gradients(weights, grads)
  return loss
# Training step
EPOCHS = 10
model.create_state(BATCH_SIZE)
for epoch in range(EPOCHS):
  start = time.time()
  # initializing the hidden state at the start of every epoch
  model.reset_state()
  for (batch_n, (inp, target)) in enumerate(dataset):
    loss = train_step(inp, target)
    if batch_n % 100 == 0:
      template = 'Epoch {} Batch {} Loss {}'
      print(template.format(epoch+1, batch_n, loss))
  print ('Epoch {} Loss {}'.format(epoch+1, loss))
  print ('Time taken for 1 epoch {} sec\n'.format(time.time() - start))
Explanation: Training loop
Again, we write our training loop from scratch.
To keep training time reasonable, use 10 epochs to train the model. In Colab, set the runtime to GPU for faster training.
End of explanation
def generate_text(model, start_string):
  # Evaluation step (generating text using the learned model)
  # Number of characters to generate
  num_generate = 1000
  # Converting our start string to numbers (vectorizing)
  input_eval = [char2idx[s] for s in start_string]
  input_eval = tf.expand_dims(input_eval, 0)
  # Empty string to store our results
  text_generated = []
  # Low temperatures results in more predictable text.
  # Higher temperatures results in more surprising text.
  # Experiment to find the best setting.
  temperature = 1.0
  # Here batch size == 1
  model.create_state(1)
  for i in range(num_generate):
      predictions = model(input_eval)
      # remove the batch dimension
      predictions = tf.squeeze(predictions, 0)
      # using a categorical distribution to predict the character returned by the model
      predictions = predictions / temperature
      predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()
      # We pass the predicted character as the next input to the model
      # along with the previous hidden state
      input_eval = tf.expand_dims([predicted_id], 0)
      text_generated.append(idx2char[predicted_id])
  return (start_string + ''.join(text_generated))
print(generate_text(model, start_string=u"ROMEO: "))
Explanation: Generate text
The following code block generates the text:
It Starts by choosing a start string, initializing the RNN state and setting the number of characters to generate.
Get the prediction distribution of the next character using the start string and the RNN state.
Then, use a categorical distribution to calculate the index of the predicted character. Use this predicted character as our next input to the model.
The RNN state returned by the model is fed back into the model so that it now has more context, instead than only one character. After predicting the next character, the modified RNN states are again fed back into the model, which is how it learns as it gets more context from the previously predicted characters.
Looking at the generated text, you'll see the model knows when to capitalize, make paragraphs and imitates a Shakespeare-like writing vocabulary. With the small number of training epochs, it has not yet learned to form coherent sentences.
To keep this prediction step simple, use a batch size of 1.
End of explanation
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