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This tutorial demonstrates how to generate text using a character-based RNN. You 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.
This tutorial includes runnable code implemented using tf.keras and eager execution. The following is the sample output when the model in this tutorial trained for 30 epochs, and started with the prompt "Q":
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
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
import tensorflow as tf
import numpy as np
import os
import time
2023-11-16 12:28:52.207051: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2023-11-16 12:28:52.207090: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2023-11-16 12:28:52.208630: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
Download the Shakespeare dataset
Change the following line to run this code on your own data.
path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')
Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt 1115394/1115394 [==============================] - 0s 0us/step
Read the data
First, look in the text:
# 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(f'Length of text: {len(text)} characters')
Length of text: 1115394 characters
# Take a look at the first 250 characters in text
print(text[:250])
First Citizen: Before we proceed any further, hear me speak. All: Speak, speak. First Citizen: You are all resolved rather to die than to famish? All: Resolved. resolved. First Citizen: First, you know Caius Marcius is chief enemy to the people.
# The unique characters in the file
vocab = sorted(set(text))
print(f'{len(vocab)} unique characters')
65 unique characters
Process the text
Vectorize the text
Before training, you need to convert the strings to a numerical representation.
The tf.keras.layers.StringLookup
layer can convert each character into a numeric ID. It just needs the text to be split into tokens first.
example_texts = ['abcdefg', 'xyz']
chars = tf.strings.unicode_split(example_texts, input_encoding='UTF-8')
chars
<tf.RaggedTensor [[b'a', b'b', b'c', b'd', b'e', b'f', b'g'], [b'x', b'y', b'z']]>
Now create the tf.keras.layers.StringLookup
layer:
ids_from_chars = tf.keras.layers.StringLookup(
vocabulary=list(vocab), mask_token=None)
It converts from tokens to character IDs:
ids = ids_from_chars(chars)
ids
<tf.RaggedTensor [[40, 41, 42, 43, 44, 45, 46], [63, 64, 65]]>
Since the goal of this tutorial is to generate text, it will also be important to invert this representation and recover human-readable strings from it. For this you can use tf.keras.layers.StringLookup(..., invert=True)
.
chars_from_ids = tf.keras.layers.StringLookup(
vocabulary=ids_from_chars.get_vocabulary(), invert=True, mask_token=None)
This layer recovers the characters from the vectors of IDs, and returns them as a tf.RaggedTensor
of characters:
chars = chars_from_ids(ids)
chars
<tf.RaggedTensor [[b'a', b'b', b'c', b'd', b'e', b'f', b'g'], [b'x', b'y', b'z']]>
You can tf.strings.reduce_join
to join the characters back into strings.
tf.strings.reduce_join(chars, axis=-1).numpy()
array([b'abcdefg', b'xyz'], dtype=object)
def text_from_ids(ids):
return tf.strings.reduce_join(chars_from_ids(ids), axis=-1)
The prediction task
Given a character, or a sequence of characters, what is the most probable next character? This is the task you're training the model to perform. The input to the model will be a sequence of characters, and you 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".
To do this first use the tf.data.Dataset.from_tensor_slices
function to convert the text vector into a stream of character indices.
all_ids = ids_from_chars(tf.strings.unicode_split(text, 'UTF-8'))
all_ids
<tf.Tensor: shape=(1115394,), dtype=int64, numpy=array([19, 48, 57, ..., 46, 9, 1])>
ids_dataset = tf.data.Dataset.from_tensor_slices(all_ids)
for ids in ids_dataset.take(10):
print(chars_from_ids(ids).numpy().decode('utf-8'))
F i r s t C i t i
seq_length = 100
The batch
method lets you easily convert these individual characters to sequences of the desired size.
sequences = ids_dataset.batch(seq_length+1, drop_remainder=True)
for seq in sequences.take(1):
print(chars_from_ids(seq))
tf.Tensor( [b'F' b'i' b'r' b's' b't' b' ' b'C' b'i' b't' b'i' b'z' b'e' b'n' b':' b'\n' b'B' b'e' b'f' b'o' b'r' b'e' b' ' b'w' b'e' b' ' b'p' b'r' b'o' b'c' b'e' b'e' b'd' b' ' b'a' b'n' b'y' b' ' b'f' b'u' b'r' b't' b'h' b'e' b'r' b',' b' ' b'h' b'e' b'a' b'r' b' ' b'm' b'e' b' ' b's' b'p' b'e' b'a' b'k' b'.' b'\n' b'\n' b'A' b'l' b'l' b':' b'\n' b'S' b'p' b'e' b'a' b'k' b',' b' ' b's' b'p' b'e' b'a' b'k' b'.' b'\n' b'\n' b'F' b'i' b'r' b's' b't' b' ' b'C' b'i' b't' b'i' b'z' b'e' b'n' b':' b'\n' b'Y' b'o' b'u' b' '], shape=(101,), dtype=string)
It's easier to see what this is doing if you join the tokens back into strings:
for seq in sequences.take(5):
print(text_from_ids(seq).numpy())
b'First Citizen:\nBefore we proceed any further, hear me speak.\n\nAll:\nSpeak, speak.\n\nFirst Citizen:\nYou ' b'are all resolved rather to die than to famish?\n\nAll:\nResolved. resolved.\n\nFirst Citizen:\nFirst, you k' b"now Caius Marcius is chief enemy to the people.\n\nAll:\nWe know't, we know't.\n\nFirst Citizen:\nLet us ki" b"ll him, and we'll have corn at our own price.\nIs't a verdict?\n\nAll:\nNo more talking on't; let it be d" b'one: away, away!\n\nSecond Citizen:\nOne word, good citizens.\n\nFirst Citizen:\nWe are accounted poor citi'
For training you'll need a dataset of (input, label)
pairs. Where input
and
label
are sequences. At each time step the input is the current character and the label is the next character.
Here's a function that takes a sequence as input, duplicates, and shifts it to align the input and label for each timestep:
def split_input_target(sequence):
input_text = sequence[:-1]
target_text = sequence[1:]
return input_text, target_text
split_input_target(list("Tensorflow"))
(['T', 'e', 'n', 's', 'o', 'r', 'f', 'l', 'o'], ['e', 'n', 's', 'o', 'r', 'f', 'l', 'o', 'w'])
dataset = sequences.map(split_input_target)
for input_example, target_example in dataset.take(1):
print("Input :", text_from_ids(input_example).numpy())
print("Target:", text_from_ids(target_example).numpy())
Input : b'First Citizen:\nBefore we proceed any further, hear me speak.\n\nAll:\nSpeak, speak.\n\nFirst Citizen:\nYou' Target: b'irst Citizen:\nBefore we proceed any further, hear me speak.\n\nAll:\nSpeak, speak.\n\nFirst Citizen:\nYou '
Create training batches
You used tf.data
to split the text into manageable sequences. But before feeding this data into the model, you need to shuffle the data and pack it into batches.
# 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)
.prefetch(tf.data.experimental.AUTOTUNE))
dataset
<_PrefetchDataset element_spec=(TensorSpec(shape=(64, 100), dtype=tf.int64, name=None), TensorSpec(shape=(64, 100), dtype=tf.int64, name=None))>
Build The Model
This section defines the model as a keras.Model
subclass (For details see Making new Layers and Models via subclassing).
This model has three layers:
tf.keras.layers.Embedding
: The input layer. A trainable lookup table that will map each character-ID to a vector withembedding_dim
dimensions;tf.keras.layers.GRU
: A type of RNN with sizeunits=rnn_units
(You can also use an LSTM layer here.)tf.keras.layers.Dense
: The output layer, withvocab_size
outputs. It outputs one logit for each character in the vocabulary. These are the log-likelihood of each character according to the model.
# Length of the vocabulary in StringLookup Layer
vocab_size = len(ids_from_chars.get_vocabulary())
# The embedding dimension
embedding_dim = 256
# Number of RNN units
rnn_units = 1024
class MyModel(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, rnn_units):
super().__init__(self)
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
self.gru = tf.keras.layers.GRU(rnn_units,
return_sequences=True,
return_state=True)
self.dense = tf.keras.layers.Dense(vocab_size)
def call(self, inputs, states=None, return_state=False, training=False):
x = inputs
x = self.embedding(x, training=training)
if states is None:
states = self.gru.get_initial_state(x)
x, states = self.gru(x, initial_state=states, training=training)
x = self.dense(x, training=training)
if return_state:
return x, states
else:
return x
model = MyModel(
vocab_size=vocab_size,
embedding_dim=embedding_dim,
rnn_units=rnn_units)
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:
for input_example_batch, target_example_batch in dataset.take(1):
example_batch_predictions = model(input_example_batch)
print(example_batch_predictions.shape, "# (batch_size, sequence_length, vocab_size)")
(64, 100, 66) # (batch_size, sequence_length, vocab_size)
In the above example the sequence length of the input is 100
but the model can be run on inputs of any length:
model.summary()
Model: "my_model" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding (Embedding) multiple 16896 gru (GRU) multiple 3938304 dense (Dense) multiple 67650 ================================================================= Total params: 4022850 (15.35 MB) Trainable params: 4022850 (15.35 MB) Non-trainable params: 0 (0.00 Byte) _________________________________________________________________
To get actual predictions from the model you need to sample from the output distribution, to get actual character indices. This distribution is defined by the logits over the character vocabulary.
Try it for the first example in the batch:
sampled_indices = tf.random.categorical(example_batch_predictions[0], num_samples=1)
sampled_indices = tf.squeeze(sampled_indices, axis=-1).numpy()
This gives us, at each timestep, a prediction of the next character index:
sampled_indices
array([15, 52, 19, 34, 6, 39, 41, 62, 50, 61, 42, 26, 29, 57, 34, 46, 12, 61, 53, 14, 26, 50, 5, 8, 29, 44, 2, 65, 62, 52, 53, 26, 25, 39, 64, 36, 53, 21, 34, 30, 12, 58, 61, 43, 38, 29, 1, 26, 47, 35, 52, 30, 10, 20, 59, 9, 11, 34, 59, 45, 56, 20, 39, 29, 46, 10, 54, 56, 57, 17, 19, 19, 14, 40, 12, 12, 4, 54, 22, 17, 31, 7, 61, 44, 56, 36, 5, 38, 30, 32, 23, 21, 52, 39, 42, 30, 42, 8, 17, 53])
Decode these to see the text predicted by this untrained model:
print("Input:\n", text_from_ids(input_example_batch[0]).numpy())
print()
print("Next Char Predictions:\n", text_from_ids(sampled_indices).numpy())
Input: b" of woman in the world,\nAy, every dram of woman's flesh is false, If she be.\n\nLEONTES:\nHold your pea" Next Char Predictions: b"BmFU'ZbwkvcMPrUg;vnAMk&-Pe zwmnMLZyWnHUQ;svdYP\nMhVmQ3Gt.:UtfqGZPg3oqrDFFAa;;$oIDR,veqW&YQSJHmZcQc-Dn"
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.
Attach an optimizer, and a loss function
The standard tf.keras.losses.sparse_categorical_crossentropy
loss function works in this case because it is applied across the last dimension of the predictions.
Because your model returns logits, you need to set the from_logits
flag.
loss = tf.losses.SparseCategoricalCrossentropy(from_logits=True)
example_batch_mean_loss = loss(target_example_batch, example_batch_predictions)
print("Prediction shape: ", example_batch_predictions.shape, " # (batch_size, sequence_length, vocab_size)")
print("Mean loss: ", example_batch_mean_loss)
Prediction shape: (64, 100, 66) # (batch_size, sequence_length, vocab_size) Mean loss: tf.Tensor(4.1884556, shape=(), dtype=float32)
A newly initialized model shouldn't be too sure of itself, the output logits should all have similar magnitudes. To confirm this you can check that the exponential of the mean loss is approximately equal to the vocabulary size. A much higher loss means the model is sure of its wrong answers, and is badly initialized:
tf.exp(example_batch_mean_loss).numpy()
65.920906
Configure the training procedure using the tf.keras.Model.compile
method. Use tf.keras.optimizers.Adam
with default arguments and the loss function.
model.compile(optimizer='adam', loss=loss)
Configure checkpoints
Use a tf.keras.callbacks.ModelCheckpoint
to ensure that checkpoints are saved during training:
# Directory where the checkpoints will be saved
checkpoint_dir = './training_checkpoints'
# Name of the checkpoint files
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_prefix,
save_weights_only=True)
Execute the training
To keep training time reasonable, use 10 epochs to train the model. In Colab, set the runtime to GPU for faster training.
EPOCHS = 20
history = model.fit(dataset, epochs=EPOCHS, callbacks=[checkpoint_callback])
Epoch 1/20 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1700137742.036116 34050 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process. 172/172 [==============================] - 12s 53ms/step - loss: 2.7219 Epoch 2/20 172/172 [==============================] - 10s 54ms/step - loss: 1.9972 Epoch 3/20 172/172 [==============================] - 11s 55ms/step - loss: 1.7187 Epoch 4/20 172/172 [==============================] - 11s 57ms/step - loss: 1.5568 Epoch 5/20 172/172 [==============================] - 11s 59ms/step - loss: 1.4579 Epoch 6/20 172/172 [==============================] - 11s 61ms/step - loss: 1.3891 Epoch 7/20 172/172 [==============================] - 12s 61ms/step - loss: 1.3356 Epoch 8/20 172/172 [==============================] - 12s 62ms/step - loss: 1.2913 Epoch 9/20 172/172 [==============================] - 11s 60ms/step - loss: 1.2501 Epoch 10/20 172/172 [==============================] - 11s 59ms/step - loss: 1.2090 Epoch 11/20 172/172 [==============================] - 11s 59ms/step - loss: 1.1693 Epoch 12/20 172/172 [==============================] - 11s 59ms/step - loss: 1.1283 Epoch 13/20 172/172 [==============================] - 11s 60ms/step - loss: 1.0859 Epoch 14/20 172/172 [==============================] - 11s 61ms/step - loss: 1.0391 Epoch 15/20 172/172 [==============================] - 11s 61ms/step - loss: 0.9928 Epoch 16/20 172/172 [==============================] - 11s 61ms/step - loss: 0.9408 Epoch 17/20 172/172 [==============================] - 11s 60ms/step - loss: 0.8888 Epoch 18/20 172/172 [==============================] - 11s 60ms/step - loss: 0.8361 Epoch 19/20 172/172 [==============================] - 11s 60ms/step - loss: 0.7840 Epoch 20/20 172/172 [==============================] - 11s 60ms/step - loss: 0.7337
Generate text
The simplest way to generate text with this model is to run it in a loop, and keep track of the model's internal state as you execute it.
Each time you call the model you pass in some text and an internal state. The model returns a prediction for the next character and its new state. Pass the prediction and state back in to continue generating text.
The following makes a single step prediction:
class OneStep(tf.keras.Model):
def __init__(self, model, chars_from_ids, ids_from_chars, temperature=1.0):
super().__init__()
self.temperature = temperature
self.model = model
self.chars_from_ids = chars_from_ids
self.ids_from_chars = ids_from_chars
# Create a mask to prevent "[UNK]" from being generated.
skip_ids = self.ids_from_chars(['[UNK]'])[:, None]
sparse_mask = tf.SparseTensor(
# Put a -inf at each bad index.
values=[-float('inf')]*len(skip_ids),
indices=skip_ids,
# Match the shape to the vocabulary
dense_shape=[len(ids_from_chars.get_vocabulary())])
self.prediction_mask = tf.sparse.to_dense(sparse_mask)
@tf.function
def generate_one_step(self, inputs, states=None):
# Convert strings to token IDs.
input_chars = tf.strings.unicode_split(inputs, 'UTF-8')
input_ids = self.ids_from_chars(input_chars).to_tensor()
# Run the model.
# predicted_logits.shape is [batch, char, next_char_logits]
predicted_logits, states = self.model(inputs=input_ids, states=states,
return_state=True)
# Only use the last prediction.
predicted_logits = predicted_logits[:, -1, :]
predicted_logits = predicted_logits/self.temperature
# Apply the prediction mask: prevent "[UNK]" from being generated.
predicted_logits = predicted_logits + self.prediction_mask
# Sample the output logits to generate token IDs.
predicted_ids = tf.random.categorical(predicted_logits, num_samples=1)
predicted_ids = tf.squeeze(predicted_ids, axis=-1)
# Convert from token ids to characters
predicted_chars = self.chars_from_ids(predicted_ids)
# Return the characters and model state.
return predicted_chars, states
one_step_model = OneStep(model, chars_from_ids, ids_from_chars)
Run it in a loop to generate some text. 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.
start = time.time()
states = None
next_char = tf.constant(['ROMEO:'])
result = [next_char]
for n in range(1000):
next_char, states = one_step_model.generate_one_step(next_char, states=states)
result.append(next_char)
result = tf.strings.join(result)
end = time.time()
print(result[0].numpy().decode('utf-8'), '\n\n' + '_'*80)
print('\nRun time:', end - start)
ROMEO: The dayning use your brodous parchemn a memaver But to my shrow against it. CURTIS: Do you think of't, leaving too? QUEEN ELIZABETH: It is, sir, let's see: to put my freedy-sorrow In the resolution of the fell as of; And she shall to the fish, whose parts-have drey'd the process or owe we eptits the abtent, That they have often been men asiel, as now I lay stoly to none of your adversary title. Nay, stay, what, nurse, shall I respect by him. Assisted with, Hadst thou depart; we should have seen some name of meat: Might from this coast was though his purpose, and they can ffor me to And lack upon your royal king. DUKE VINCENTIO: It is now pale; but not a man with winds, And breathed sunshine way: in give of less, the nurse In thy extreme budden and his wives. One more, most noble friend. Third Servingman: I have no more of it. FRIAR LAURENCE: O, she is found withal. Hark ye and given me thence at we? or die among thes? Evermother, Thomas, Duke old York and him, So fast aspected: s ________________________________________________________________________________ Run time: 2.878746271133423
The easiest thing you can do to improve the results is to train it for longer (try EPOCHS = 30
).
You can also experiment with a different start string, try adding another RNN layer to improve the model's accuracy, or adjust the temperature parameter to generate more or less random predictions.
If you want the model to generate text faster the easiest thing you can do is batch the text generation. In the example below the model generates 5 outputs in about the same time it took to generate 1 above.
start = time.time()
states = None
next_char = tf.constant(['ROMEO:', 'ROMEO:', 'ROMEO:', 'ROMEO:', 'ROMEO:'])
result = [next_char]
for n in range(1000):
next_char, states = one_step_model.generate_one_step(next_char, states=states)
result.append(next_char)
result = tf.strings.join(result)
end = time.time()
print(result, '\n\n' + '_'*80)
print('\nRun time:', end - start)
tf.Tensor( [b"ROMEO:\nFirst, heaven leave us: O, rest thy wild,\nI will my father dead?\n\nTRANIO:\nBut did you send us run; lay, fool!\nI would the world say no; what brought to? hear?\n\nKATHARINA:\nFear you the heavess to take away the time\nof a kiss of sold murder, to keep this luck\nWhose uncle branches order, I cry you\ndo, and the moting father of the like days\nThey shall seem from reason, whilst I live or else\nTo raise his writing fallent to speak.\nBut, Clifford, he is gone unto these wents were faults.\n\nLUCENTIO:\nAh, Warwick, art thou hear, the worst of in my false\nMoth thy meaning brave through the seas.\nIn what occasion not thyself?\n\nMONTAGUE:\nGood queen; Antigons, and brave amazed at the\ngreaty will out-plane. Calm those that valiant quench,\nthough us't! it is your baseness. Come, sit down;\nFor, madam: son, away! Now, by the worst.\n\nAll:\nTeems as it hath mest.\n\nDORSET:\nAnd love as you; I could heed home.\nAlas! I needs must out, as thou art a\nfraitors hate than for the year, and the conquer'd bowes\nIn gro" b"ROMEO:\n\nJULIET:\nAs well return.\n\nShepherd:\nBut, good my lord,\nThe Duke of Clarence hath so\n't born: but all, within mine own absence\nI married my face, moved nor boy;\nAnd I from Deepany, advangal keeps and cheer\nOf heaven nor power, at some thread.\nThat English jedwats shall not stuck a dream;\nFairer that proud thoughts about the abonty of the crown.\n\nJULIET:\nThen, I may have now, she you seal the story\nWhere nowing an oath with lips.\n\nKING RICHARD II:\nDiscourse of any good conceit? Abas!\nMy gracious sovereitn, ladies that vaunts in\nhis wonder age; but yet we should hear\nMy words shed by, and thought on sue the word,\nFor, lords, to-marry my greatness: if my trotubous lies?\n\nLADY GREY:\nTo his submission. Lady and Darb.\n\nDUCHESS OF YORK:\nWouldst thou go? then I'll judge my tongue,\nAnd graced trembling of the widdows, or\nshe's the cause? a coucle's hand thither? O!\nBelance oph?\n\nESCALUS:\nAy; for thou wert kill'd\nwhen the wall's power I could's high a peal: be corn\nAt o'cropph the instrument of " b"ROMEO:\nOr else be swellest Edward: if this long-impellow:\nAll unacounted said Lecious honour have many sortons\nThat first we all go men.\n\nDUKE OF YORK:\nWelcome, my lord, widow!\n\nALONS:\nIf it be not; she was not made a queen,\n'This people will still have mine eyes to heaven from his wife.\n\nTHOMAS MOWBRAY:\nAy, ay.\n\nCitizens:\nDown, ladies,--which is my name is Edward.\nWhen yet she finds alone.\n\nBRAKENBURIO:\nBoth, young and older,, marry; nor my countenance, I did see\nForbidst unreasonably of our queen,--\n\nSTAN:\nI know, I thank thee, knew whose heads the rest,\nBut that the rodut of this speech,\nIs this of noble things you have.\n\nDUKE OF YORK:\nAy, so: you are hereafter, it may make you an\na-bow-forth. But O, pity!\nMake your affections are up, Signior Lucentio.\n\nLUCENTIO:\nIt may not pass:\nThen we shall be most well. but drawly pay\nMe all the world's shore: Alack the dire dream of your sword,\nOne that our kingdom to the head of monsminy.\n\nPRINCE EDWARD:\nAn is her fawling! here's your done,\nHe Canst" b"ROMEO:\nLet me see: there were heard.\n\nGLOUCESTER:\n\nKING EDWARD IV:\nAn beast my wife's will obey: how meaning in him, sin\nJest, and thou shalt smile at the fashion;\nFor inholence my most courtiesy, Warwick, steble,\nThou mayst not, sawn, Kate, and then Oxford's vast\nIs sworn to lay at honours on my head\nOf her travel the curses; but we shall bear it.\n\nROMEO:\nNot one would please, as I brother!\n\nCLAUDIO:\nPett up, I,\nWe cannot white of this.\n\nRIVERS:\nAy, for once whither that's no remembrance?\nThou ammonest not most partly keeps, new,\nShould we were cross-wounded to sadver a lord,\nThe horn and holy thank your soldiers arm,\nYou advice for his head to Margar to resign his cloace:\nSure thy wife indeed are womanish.\n\nLUCENTIO:\nThen, good my lord, what talk I possess;\nAnd for the county will he did bear\nOf free speech; and will assect thee,\nThat stands the thrawhilderful: whom I do, an either painted stain\nThe spirits of their power. If you requires a mettain,\nYour mess arrormmeth in either. But we m" b"ROMEO:\nWhy, so; farewell: one in, another death or dread\nWhen he were leons.\nSo first less thou the time 'twere my badied known,\nChequering to you.\n\nShepherd:\nGo heaven and false, she's highbell'd.\n\nGLOUCESTER:\nCome, go with you, be with him, if the condaction tears\nHe thither was post to revenge,\nAnd thither shall we could grace to the white:\nThou art to-more oraple! Rise of it!\n\nCOMINIUS:\nKeere yes; if we all shall fork\nAs every cousin, drawn.\n\nPRINCE EDWARD:\nLet me unkiss! my master is safe, Furils, take your power\nTo embraceous have at the goor humour and as yours,\nAnd pluck my babes against your tears, and there before the\ncut; or if she were hered betwixt his ease.'\n\nGLOUCESTER:\nHe reverend guilt return.\n\nPARIS:\nIn vain for your name\nIs not denied and drooping too.\n\nQUEEN MARGARET:\nWhat, thou? whence could not, how?\nSail note or two!\nWho stands the matter:--Now, payied and recensit, anst\nthen to fear every end on the matom, I\nam proud to meet your gate upon her mastray's.\n\nFLORIZEL:\nDo"], shape=(5,), dtype=string) ________________________________________________________________________________ Run time: 2.8853743076324463
Export the generator
This single-step model can easily be saved and restored, allowing you to use it anywhere a tf.saved_model
is accepted.
tf.saved_model.save(one_step_model, 'one_step')
one_step_reloaded = tf.saved_model.load('one_step')
WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.OneStep object at 0x7f1a9c2e6880>, because it is not built. WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config. WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config. INFO:tensorflow:Assets written to: one_step/assets INFO:tensorflow:Assets written to: one_step/assets
states = None
next_char = tf.constant(['ROMEO:'])
result = [next_char]
for n in range(100):
next_char, states = one_step_reloaded.generate_one_step(next_char, states=states)
result.append(next_char)
print(tf.strings.join(result)[0].numpy().decode("utf-8"))
ROMEO: While shall we toward them, gaunt and married. Urped me say I doubt not, for this world is gentle,
Advanced: Customized Training
The above training procedure is simple, but does not give you much control. It uses teacher-forcing which prevents bad predictions from being fed back to the model, so the model never learns to recover from mistakes.
So now that you've seen how to run the model manually next you'll implement the training loop. This gives a starting point if, for example, you want to implement curriculum learning to help stabilize the model's open-loop output.
The most important part of a custom training loop is the train step function.
Use tf.GradientTape
to track the gradients. You can learn more about this approach by reading the eager execution guide.
The basic procedure is:
- Execute the model and calculate the loss under a
tf.GradientTape
. - Calculate the updates and apply them to the model using the optimizer.
class CustomTraining(MyModel):
@tf.function
def train_step(self, inputs):
inputs, labels = inputs
with tf.GradientTape() as tape:
predictions = self(inputs, training=True)
loss = self.loss(labels, predictions)
grads = tape.gradient(loss, model.trainable_variables)
self.optimizer.apply_gradients(zip(grads, model.trainable_variables))
return {'loss': loss}
The above implementation of the train_step
method follows Keras' train_step
conventions. This is optional, but it allows you to change the behavior of the train step and still use keras' Model.compile
and Model.fit
methods.
model = CustomTraining(
vocab_size=len(ids_from_chars.get_vocabulary()),
embedding_dim=embedding_dim,
rnn_units=rnn_units)
model.compile(optimizer = tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True))
model.fit(dataset, epochs=1)
172/172 [==============================] - 13s 58ms/step - loss: 2.7075 <keras.src.callbacks.History at 0x7f1a9c1b8190>
Or if you need more control, you can write your own complete custom training loop:
EPOCHS = 10
mean = tf.metrics.Mean()
for epoch in range(EPOCHS):
start = time.time()
mean.reset_states()
for (batch_n, (inp, target)) in enumerate(dataset):
logs = model.train_step([inp, target])
mean.update_state(logs['loss'])
if batch_n % 50 == 0:
template = f"Epoch {epoch+1} Batch {batch_n} Loss {logs['loss']:.4f}"
print(template)
# saving (checkpoint) the model every 5 epochs
if (epoch + 1) % 5 == 0:
model.save_weights(checkpoint_prefix.format(epoch=epoch))
print()
print(f'Epoch {epoch+1} Loss: {mean.result().numpy():.4f}')
print(f'Time taken for 1 epoch {time.time() - start:.2f} sec')
print("_"*80)
model.save_weights(checkpoint_prefix.format(epoch=epoch))
Epoch 1 Batch 0 Loss 2.1913 Epoch 1 Batch 50 Loss 2.0591 Epoch 1 Batch 100 Loss 1.9363 Epoch 1 Batch 150 Loss 1.8937 Epoch 1 Loss: 1.9814 Time taken for 1 epoch 12.44 sec ________________________________________________________________________________ Epoch 2 Batch 0 Loss 1.8354 Epoch 2 Batch 50 Loss 1.7515 Epoch 2 Batch 100 Loss 1.6990 Epoch 2 Batch 150 Loss 1.6749 Epoch 2 Loss: 1.7090 Time taken for 1 epoch 11.45 sec ________________________________________________________________________________ Epoch 3 Batch 0 Loss 1.5859 Epoch 3 Batch 50 Loss 1.5785 Epoch 3 Batch 100 Loss 1.5548 Epoch 3 Batch 150 Loss 1.5089 Epoch 3 Loss: 1.5524 Time taken for 1 epoch 11.49 sec ________________________________________________________________________________ Epoch 4 Batch 0 Loss 1.4963 Epoch 4 Batch 50 Loss 1.4674 Epoch 4 Batch 100 Loss 1.4629 Epoch 4 Batch 150 Loss 1.4254 Epoch 4 Loss: 1.4550 Time taken for 1 epoch 11.26 sec ________________________________________________________________________________ Epoch 5 Batch 0 Loss 1.3884 Epoch 5 Batch 50 Loss 1.4480 Epoch 5 Batch 100 Loss 1.3669 Epoch 5 Batch 150 Loss 1.3619 Epoch 5 Loss: 1.3870 Time taken for 1 epoch 11.19 sec ________________________________________________________________________________ Epoch 6 Batch 0 Loss 1.3157 Epoch 6 Batch 50 Loss 1.3346 Epoch 6 Batch 100 Loss 1.3065 Epoch 6 Batch 150 Loss 1.2660 Epoch 6 Loss: 1.3341 Time taken for 1 epoch 11.25 sec ________________________________________________________________________________ Epoch 7 Batch 0 Loss 1.3223 Epoch 7 Batch 50 Loss 1.2794 Epoch 7 Batch 100 Loss 1.2886 Epoch 7 Batch 150 Loss 1.3036 Epoch 7 Loss: 1.2888 Time taken for 1 epoch 11.10 sec ________________________________________________________________________________ Epoch 8 Batch 0 Loss 1.2318 Epoch 8 Batch 50 Loss 1.2245 Epoch 8 Batch 100 Loss 1.2677 Epoch 8 Batch 150 Loss 1.2397 Epoch 8 Loss: 1.2480 Time taken for 1 epoch 11.13 sec ________________________________________________________________________________ Epoch 9 Batch 0 Loss 1.2021 Epoch 9 Batch 50 Loss 1.2654 Epoch 9 Batch 100 Loss 1.2190 Epoch 9 Batch 150 Loss 1.1929 Epoch 9 Loss: 1.2083 Time taken for 1 epoch 11.31 sec ________________________________________________________________________________ Epoch 10 Batch 0 Loss 1.1429 Epoch 10 Batch 50 Loss 1.1642 Epoch 10 Batch 100 Loss 1.1455 Epoch 10 Batch 150 Loss 1.1687 Epoch 10 Loss: 1.1684 Time taken for 1 epoch 11.55 sec ________________________________________________________________________________