Skip to content

Instantly share code, notes, and snippets.

@b03902043
Last active March 15, 2021 10:36
Show Gist options
  • Star 2 You must be signed in to star a gist
  • Fork 3 You must be signed in to fork a gist
  • Save b03902043/831477deda86725cc3a3ae28e6dbb9a5 to your computer and use it in GitHub Desktop.
Save b03902043/831477deda86725cc3a3ae28e6dbb9a5 to your computer and use it in GitHub Desktop.
Video captioning ( Seq2Seq in Keras )
from keras import backend as K
from keras.layers import TimeDistributed, Dense, LSTM
class AttentionLSTM(LSTM):
"""LSTM with attention mechanism
This is an LSTM incorporating an attention mechanism into its hidden states.
Currently, the context vector calculated from the attended vector is fed
into the model's internal states, closely following the model by Xu et al.
(2016, Sec. 3.1.2), using a soft attention model following
Bahdanau et al. (2014).
The layer expects two inputs instead of the usual one:
1. the "normal" layer input; and
2. a 3D vector to attend.
Args:
attn_activation: Activation function for attentional components
attn_init: Initialization function for attention weights
output_alpha (boolean): If true, outputs the alpha values, i.e.,
what parts of the attention vector the layer attends to at each
timestep.
References:
* Bahdanau, Cho & Bengio (2014), "Neural Machine Translation by Jointly
Learning to Align and Translate", <https://arxiv.org/pdf/1409.0473.pdf>
* Xu, Ba, Kiros, Cho, Courville, Salakhutdinov, Zemel & Bengio (2016),
"Show, Attend and Tell: Neural Image Caption Generation with Visual
Attention", <http://arxiv.org/pdf/1502.03044.pdf>
See Also:
`LSTM`_ in the Keras documentation.
.. _LSTM: http://keras.io/layers/recurrent/#lstm
"""
def __init__(self, *args, attn_activation='tanh', attn_init='orthogonal',
output_alpha=False, **kwargs):
self.attn_activation = activations.get(attn_activation)
self.attn_init = initializations.get(attn_init)
self.output_alpha = output_alpha
super().__init__(*args, **kwargs)
def build(self, input_shape):
if not (isinstance(input_shape, list) and len(input_shape) == 2):
raise Exception('Input to AttentionLSTM must be a list of '
'two tensors [lstm_input, attn_input].')
input_shape, attn_input_shape = input_shape
super().build(input_shape)
self.input_spec.append(InputSpec(shape=attn_input_shape))
# weights for attention model
self.U_att = self.inner_init((self.output_dim, self.output_dim),
name='{}_U_att'.format(self.name))
self.W_att = self.attn_init((attn_input_shape[-1], self.output_dim),
name='{}_W_att'.format(self.name))
self.v_att = self.init((self.output_dim, 1),
name='{}_v_att'.format(self.name))
self.b_att = K.zeros((self.output_dim,), name='{}_b_att'.format(self.name))
self.trainable_weights += [self.U_att, self.W_att, self.v_att, self.b_att]
# weights for incorporating attention into hidden states
if self.consume_less == 'gpu':
self.Z = self.init((attn_input_shape[-1], 4 * self.output_dim),
name='{}_Z'.format(self.name))
self.trainable_weights += [self.Z]
else:
self.Z_i = self.attn_init((attn_input_shape[-1], self.output_dim),
name='{}_Z_i'.format(self.name))
self.Z_f = self.attn_init((attn_input_shape[-1], self.output_dim),
name='{}_Z_f'.format(self.name))
self.Z_c = self.attn_init((attn_input_shape[-1], self.output_dim),
name='{}_Z_c'.format(self.name))
self.Z_o = self.attn_init((attn_input_shape[-1], self.output_dim),
name='{}_Z_o'.format(self.name))
self.trainable_weights += [self.Z_i, self.Z_f, self.Z_c, self.Z_o]
self.Z = K.concatenate([self.Z_i, self.Z_f, self.Z_c, self.Z_o])
# weights for initializing states based on attention vector
if not self.stateful:
self.W_init_c = self.attn_init((attn_input_shape[-1], self.output_dim),
name='{}_W_init_c'.format(self.name))
self.W_init_h = self.attn_init((attn_input_shape[-1], self.output_dim),
name='{}_W_init_h'.format(self.name))
self.b_init_c = K.zeros((self.output_dim,),
name='{}_b_init_c'.format(self.name))
self.b_init_h = K.zeros((self.output_dim,),
name='{}_b_init_h'.format(self.name))
self.trainable_weights += [self.W_init_c, self.b_init_c,
self.W_init_h, self.b_init_h]
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
def get_output_shape_for(self, input_shape):
# output shape is not affected by the attention component
return super().get_output_shape_for(input_shape[0])
def compute_mask(self, input, input_mask=None):
if input_mask is not None:
input_mask = input_mask[0]
return super().compute_mask(input, input_mask=input_mask)
def get_initial_states(self, x_input, x_attn, mask_attn):
# set initial states from mean attention vector fed through a dense
# activation
mean_attn = K.mean(x_attn * K.expand_dims(mask_attn), axis=1)
h0 = K.dot(mean_attn, self.W_init_h) + self.b_init_h
c0 = K.dot(mean_attn, self.W_init_c) + self.b_init_c
return [self.attn_activation(h0), self.attn_activation(c0)]
def call(self, x, mask=None):
assert isinstance(x, list) and len(x) == 2
x_input, x_attn = x
if mask is not None:
mask_input, mask_attn = mask
else:
mask_input, mask_attn = None, None
# input shape: (nb_samples, time (padded with zeros), input_dim)
input_shape = self.input_spec[0].shape
if K._BACKEND == 'tensorflow':
if not input_shape[1]:
raise Exception('When using TensorFlow, you should define '
'explicitly the number of timesteps of '
'your sequences.\n'
'If your first layer is an Embedding, '
'make sure to pass it an "input_length" '
'argument. Otherwise, make sure '
'the first layer has '
'an "input_shape" or "batch_input_shape" '
'argument, including the time axis. '
'Found input shape at layer ' + self.name +
': ' + str(input_shape))
if self.stateful:
initial_states = self.states
else:
initial_states = self.get_initial_states(x_input, x_attn, mask_attn)
constants = self.get_constants(x_input, x_attn, mask_attn)
preprocessed_input = self.preprocess_input(x_input)
last_output, outputs, states = K.rnn(self.step, preprocessed_input,
initial_states,
go_backwards=self.go_backwards,
mask=mask_input,
constants=constants,
unroll=self.unroll,
input_length=input_shape[1])
if self.stateful:
self.updates = []
for i in range(len(states)):
self.updates.append((self.states[i], states[i]))
if self.return_sequences:
return outputs
else:
return last_output
def step(self, x, states):
h_tm1 = states[0]
c_tm1 = states[1]
B_U = states[2]
B_W = states[3]
x_attn = states[4]
mask_attn = states[5]
attn_shape = self.input_spec[1].shape
#### attentional component
# alignment model
# -- keeping weight matrices for x_attn and h_s separate has the advantage
# that the feature dimensions of the vectors can be different
h_att = K.repeat(h_tm1, attn_shape[1])
att = TimeDistributed(Dense(x_attn, self.W_att, self.b_att))
energy = self.attn_activation(K.dot(h_att, self.U_att) + att)
energy = K.squeeze(K.dot(energy, self.v_att), 2)
# make probability tensor
alpha = K.exp(energy)
if mask_attn is not None:
alpha *= mask_attn
alpha /= K.sum(alpha, axis=1, keepdims=True)
alpha_r = K.repeat(alpha, attn_shape[2])
alpha_r = K.permute_dimensions(alpha_r, (0, 2, 1))
# make context vector -- soft attention after Bahdanau et al.
z_hat = x_attn * alpha_r
z_hat = K.sum(z_hat, axis=1)
if self.consume_less == 'gpu':
z = K.dot(x * B_W[0], self.W) + K.dot(h_tm1 * B_U[0], self.U) \
+ K.dot(z_hat, self.Z) + self.b
z0 = z[:, :self.output_dim]
z1 = z[:, self.output_dim: 2 * self.output_dim]
z2 = z[:, 2 * self.output_dim: 3 * self.output_dim]
z3 = z[:, 3 * self.output_dim:]
else:
if self.consume_less == 'cpu':
x_i = x[:, :self.output_dim]
x_f = x[:, self.output_dim: 2 * self.output_dim]
x_c = x[:, 2 * self.output_dim: 3 * self.output_dim]
x_o = x[:, 3 * self.output_dim:]
elif self.consume_less == 'mem':
x_i = K.dot(x * B_W[0], self.W_i) + self.b_i
x_f = K.dot(x * B_W[1], self.W_f) + self.b_f
x_c = K.dot(x * B_W[2], self.W_c) + self.b_c
x_o = K.dot(x * B_W[3], self.W_o) + self.b_o
else:
raise Exception('Unknown `consume_less` mode.')
z0 = x_i + K.dot(h_tm1 * B_U[0], self.U_i) + K.dot(z_hat, self.Z_i)
z1 = x_f + K.dot(h_tm1 * B_U[1], self.U_f) + K.dot(z_hat, self.Z_f)
z2 = x_c + K.dot(h_tm1 * B_U[2], self.U_c) + K.dot(z_hat, self.Z_c)
z3 = x_o + K.dot(h_tm1 * B_U[3], self.U_o) + K.dot(z_hat, self.Z_o)
i = self.inner_activation(z0)
f = self.inner_activation(z1)
c = f * c_tm1 + i * self.activation(z2)
o = self.inner_activation(z3)
h = o * self.activation(c)
if self.output_alpha:
return alpha, [h, c]
else:
return h, [h, c]
def get_constants(self, x_input, x_attn, mask_attn):
constants = super().get_constants(x_input)
attn_shape = self.input_spec[1].shape
if mask_attn is not None:
if K.ndim(mask_attn) == 3:
mask_attn = K.all(mask_attn, axis=-1)
constants.append(x_attn)
constants.append(mask_attn)
return constants
def get_config(self):
cfg = super().get_config()
cfg['output_alpha'] = self.output_alpha
cfg['attn_activation'] = self.attn_activation.__name__
return cfg
@classmethod
def from_config(cls, config):
instance = super(AttentionLSTM, cls).from_config(config)
if 'output_alpha' in config:
instance.output_alpha = config['output_alpha']
if 'attn_activation' in config:
instance.attn_activation = activations.get(config['attn_activation'])
return instance
https://github.com/fchollet/keras/issues/4962
https://gist.github.com/mbollmann/ccc735366221e4dba9f89d2aab86da1e
https://gist.github.com/mbollmann/29fd21931820c64095617125824ea246
https://github.com/fchollet/keras/issues/2995
https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
https://docs.google.com/presentation/d/1O0eohFR5J4AStQVAyaJOfLXw2CO_KEOC_BL75MnxxV4/edit#slide=id.g2910edee95_0_150
beam search
https://arxiv.org/pdf/1606.02960.pdf
schedule sampling
https://arxiv.org/pdf/1506.03099.pdf
'''Sequence to sequence example in Keras (character-level).
This script demonstrates how to implement a basic character-level
sequence-to-sequence model. We apply it to translating
short English sentences into short French sentences,
character-by-character. Note that it is fairly unusual to
do character-level machine translation, as word-level
models are more common in this domain.
# Summary of the algorithm:
- We start with input sequences from a domain (e.g. English sentences)
and correspding target sequences from another domain
(e.g. French sentences).
- An encoder LSTM turns input sequences to 2 state vectors
(we keep the last LSTM state and discard the outputs).
- A decoder LSTM is trained to turn the target sequences into
the same sequence but offset by one timestep in the future,
a training process called "teacher forcing" in this context.
Is uses as initial state the state vectors from the encoder.
Effectively, the decoder learns to generate `targets[t+1...]`
given `targets[...t]`, conditioned on the input sequence.
- In inference mode, when we want to decode unknown input sequences, we:
- Encode the input sequence into state vectors
- Start with a target sequence of size 1
(just the start-of-sequence character)
- Feed the state vectors and 1-char target sequence
to the decoder to produce predictions for the next character
- Sample the next character using these predictions
(we simply use argmax).
- Append the sampled character to the target sequence
- Repeat until we generate the end-of-sequence character or we
hit the character limit.
# Data download:
English to French sentence pairs.
http://www.manythings.org/anki/fra-eng.zip
Lots of neat sentence pairs datasets can be found at:
http://www.manythings.org/anki/
# References:
- Sequence to Sequence Learning with Neural Networks
https://arxiv.org/abs/1409.3215
- Learning Phrase Representations using
RNN Encoder-Decoder for Statistical Machine Translation
https://arxiv.org/abs/1406.1078
'''
from __future__ import print_function
from keras.models import Model
from keras.layers import Input, LSTM, Dense
import numpy as np
from AttentionLSTM import AttentionLSTM
batch_size = 64 # Batch size for training.
epochs = 100 # Number of epochs to train for.
latent_dim = 256 # Latent dimensionality of the encoding space.
num_samples = 10000 # Number of samples to train on.
# Path to the data txt file on disk.
data_path = 'fra-eng/fra.txt'
# Vectorize the data.
input_texts = []
target_texts = []
input_characters = set()
target_characters = set()
lines = open(data_path).read().split('\n')
for line in lines[: min(num_samples, len(lines) - 1)]:
input_text, target_text = line.split('\t')
# We use "tab" as the "start sequence" character
# for the targets, and "\n" as "end sequence" character.
target_text = '\t' + target_text + '\n'
input_texts.append(input_text)
target_texts.append(target_text)
for char in input_text:
if char not in input_characters:
input_characters.add(char)
for char in target_text:
if char not in target_characters:
target_characters.add(char)
input_characters = sorted(list(input_characters))
target_characters = sorted(list(target_characters))
num_encoder_tokens = len(input_characters) # 4096
num_decoder_tokens = len(target_characters) # 3000
max_encoder_seq_length = max([len(txt) for txt in input_texts]) # 80
max_decoder_seq_length = max([len(txt) for txt in target_texts]) # 20
print('Number of samples:', len(input_texts))
print('Number of unique input tokens:', num_encoder_tokens)
print('Number of unique output tokens:', num_decoder_tokens)
print('Max sequence length for inputs:', max_encoder_seq_length)
print('Max sequence length for outputs:', max_decoder_seq_length)
input_token_index = dict(
[(char, i) for i, char in enumerate(input_characters)])
target_token_index = dict(
[(char, i) for i, char in enumerate(target_characters)])
encoder_input_data = np.zeros(
(len(input_texts), max_encoder_seq_length, num_encoder_tokens),
dtype='float32')
decoder_input_data = np.zeros(
(len(input_texts), max_decoder_seq_length, num_decoder_tokens),
dtype='float32')
decoder_target_data = np.zeros(
(len(input_texts), max_decoder_seq_length, num_decoder_tokens),
dtype='float32')
for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
for t, char in enumerate(input_text):
encoder_input_data[i, t, input_token_index[char]] = 1.
for t, char in enumerate(target_text):
# decoder_target_data is ahead of decoder_input_data by one timestep
decoder_input_data[i, t, target_token_index[char]] = 1.
if t > 0:
# decoder_target_data will be ahead by one timestep
# and will not include the start character.
decoder_target_data[i, t - 1, target_token_index[char]] = 1.
# feat : 1450*M x 80 x 4096
# label: 1450*M x 20 x 3000 (max decode timestep = 20)
############ teacher forcing
# Define an input sequence and process it.
encoder_inputs = Input(shape=(None, num_encoder_tokens))
encoder = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
# We discard `encoder_outputs` and only keep the states.
encoder_states = [state_h, state_c]
# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = Input(shape=(None, num_decoder_tokens))
# We set up our decoder to return full output sequences,
# and to return internal states as well. We don't use the
# return states in the training model, but we will use them in inference.
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs,
initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)
#############
#################### non-teacher forcing
from keras.layers import Lambda
from keras import backend as K
# The first part is unchanged
encoder_inputs = Input(shape=(None, num_encoder_tokens))
encoder = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
states = [state_h, state_c]
# Set up the decoder, which will only process one timestep at a time.
decoder_inputs = Input(shape=(1, num_decoder_tokens))
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
all_outputs = []
inputs = decoder_inputs
for _ in range(max_decoder_seq_length):
# Run the decoder on one timestep
outputs, state_h, state_c = decoder_lstm(inputs,
initial_state=states)
outputs = decoder_dense(outputs)
# Store the current prediction (we will concatenate all predictions later)
all_outputs.append(outputs)
# Reinject the outputs as inputs for the next loop iteration
# as well as update the states
inputs = outputs
states = [state_h, state_c]
# Concatenate all predictions
decoder_outputs = Lambda(lambda x: K.concatenate(x, axis=1))(all_outputs)
# Prepare decoder input data that just contains the start character
# Note that we could have made it a constant hard-coded in the model
decoder_input_data = np.zeros((num_samples, 1, num_decoder_tokens))
decoder_input_data[:, 0, target_token_index['\t']] = 1.
####################
# Define the model that will turn
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
# Run training
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2)
# Save model
model.save('s2s.h5')
# Next: inference mode (sampling).
# Here's the drill:
# 1) encode input and retrieve initial decoder state
# 2) run one step of decoder with this initial state
# and a "start of sequence" token as target.
# Output will be the next target token
# 3) Repeat with the current target token and current states
# Define sampling models
encoder_model = Model(encoder_inputs, encoder_states)
decoder_state_input_h = Input(shape=(latent_dim,))
decoder_state_input_c = Input(shape=(latent_dim,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_outputs, state_h, state_c = decoder_lstm(
decoder_inputs, initial_state=decoder_states_inputs)
decoder_states = [state_h, state_c]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = Model(
[decoder_inputs] + decoder_states_inputs,
[decoder_outputs] + decoder_states)
# Reverse-lookup token index to decode sequences back to
# something readable.
reverse_input_char_index = dict(
(i, char) for char, i in input_token_index.items())
reverse_target_char_index = dict(
(i, char) for char, i in target_token_index.items())
def decode_sequence(input_seq):
# Encode the input as state vectors.
states_value = encoder_model.predict(input_seq)
# Generate empty target sequence of length 1.
target_seq = np.zeros((1, 1, num_decoder_tokens))
# Populate the first character of target sequence with the start character.
target_seq[0, 0, target_token_index['\t']] = 1.
# Sampling loop for a batch of sequences
# (to simplify, here we assume a batch of size 1).
stop_condition = False
decoded_sentence = ''
while not stop_condition:
output_tokens, h, c = decoder_model.predict(
[target_seq] + states_value)
# Sample a token
sampled_token_index = np.argmax(output_tokens[0, -1, :])
sampled_char = reverse_target_char_index[sampled_token_index]
decoded_sentence += sampled_char
# Exit condition: either hit max length
# or find stop character.
if (sampled_char == '\n' or
len(decoded_sentence) > max_decoder_seq_length):
stop_condition = True
# Update the target sequence (of length 1).
target_seq = np.zeros((1, 1, num_decoder_tokens))
target_seq[0, 0, sampled_token_index] = 1.
# Update states
states_value = [h, c]
return decoded_sentence
for seq_index in range(100):
# Take one sequence (part of the training test)
# for trying out decoding.
input_seq = encoder_input_data[seq_index: seq_index + 1]
decoded_sentence = decode_sequence(input_seq)
print('-')
print('Input sentence:', input_texts[seq_index])
print('Decoded sentence:', decoded_sentence)
@alokssingh
Copy link

Hey nice implementation !!
can you tell the which version of Keras does this code support because I'am getting error in running this code

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment