标签：label more complex different written app tom new t sof
Welcome to your first programming assignment for this week!
This notebook was produced together with NVIDIA's Deep Learning Institute.
define optimizer and compile model
Spelling, grammar and wording corrections.
Let's load all the packages you will need for this assignment.
from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply
from keras.layers import RepeatVector, Dense, Activation, Lambda
from keras.optimizers import Adam
from keras.utils import to_categorical
from keras.models import load_model, Model
import keras.backend as K
import numpy as np
from faker import Faker
import random
from tqdm import tqdm
from babel.dates import format_date
from nmt_utils import *
import matplotlib.pyplot as plt
%matplotlib inline
Using TensorFlow backend.
We will train the model on a dataset of 10,000 human readable dates and their equivalent, standardized, machine readable dates. Let's run the following cells to load the dataset and print some examples.
m = 10000
dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset(m)
100%|██████████| 10000/10000 [00:00<00:00, 18645.80it/s]
dataset[:10]
[('9 may 1998', '1998-05-09'),
('10.11.19', '2019-11-10'),
('9/10/70', '1970-09-10'),
('saturday april 28 1990', '1990-04-28'),
('thursday january 26 1995', '1995-01-26'),
('monday march 7 1983', '1983-03-07'),
('sunday may 22 1988', '1988-05-22'),
('08 jul 2008', '2008-07-08'),
('8 sep 1999', '1999-09-08'),
('thursday january 1 1981', '1981-01-01')]
You've loaded:
dataset: a list of tuples of (human readable date, machine readable date).human_vocab: a python dictionary mapping all characters used in the human readable dates to an integer-valued index.machine_vocab: a python dictionary mapping all characters used in machine readable dates to an integer-valued index.
human_vocab. inv_machine_vocab: the inverse dictionary of machine_vocab, mapping from indices back to characters. Let's preprocess the data and map the raw text data into the index values.
Tx = 30
Ty = 10
X, Y, Xoh, Yoh = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty)
print("X.shape:", X.shape)
print("Y.shape:", Y.shape)
print("Xoh.shape:", Xoh.shape)
print("Yoh.shape:", Yoh.shape)
X.shape: (10000, 30)
Y.shape: (10000, 10)
Xoh.shape: (10000, 30, 37)
Yoh.shape: (10000, 10, 11)
You now have:
X: a processed version of the human readable dates in the training set.
human_vocab. X.shape = (m, Tx) where m is the number of training examples in a batch.Y: a processed version of the machine readable dates in the training set.
machine_vocab. Y.shape = (m, Ty). Xoh: one-hot version of X
X is converted to the one-hot representation (if the index is 2, the one-hot version has the index position 2 set to 1, and the remaining positions are 0.Xoh.shape = (m, Tx, len(human_vocab))Yoh: one-hot version of Y
Y is converted to the one-hot representation. Yoh.shape = (m, Tx, len(machine_vocab)). len(machine_vocab) = 11 since there are 10 numeric digits (0 to 9) and the - symbol.Let's also look at some examples of preprocessed training examples.
Feel free to play with index in the cell below to navigate the dataset and see how source/target dates are preprocessed.
index = 0
print("Source date:", dataset[index][0])
print("Target date:", dataset[index][1])
print()
print("Source after preprocessing (indices):", X[index])
print("Target after preprocessing (indices):", Y[index])
print()
print("Source after preprocessing (one-hot):", Xoh[index])
print("Target after preprocessing (one-hot):", Yoh[index])
Source date: 9 may 1998
Target date: 1998-05-09
Source after preprocessing (indices): [12 0 24 13 34 0 4 12 12 11 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36
36 36 36 36 36]
Target after preprocessing (indices): [ 2 10 10 9 0 1 6 0 1 10]
Source after preprocessing (one-hot): [[ 0. 0. 0. ..., 0. 0. 0.]
[ 1. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 1.]
[ 0. 0. 0. ..., 0. 0. 1.]
[ 0. 0. 0. ..., 0. 0. 1.]]
Target after preprocessing (one-hot): [[ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
[ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
[ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]
In this part, you will implement the attention mechanism presented in the lecture videos.
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style="width:500 |
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Here are some properties of the model that you may notice:
Post-attention LSTM: at the top of the diagram comes after the attention mechanism.
The post-attention LSTM passes the hidden state \(s^{\langle t \rangle}\) and cell state \(c^{\langle t \rangle}\) from one time step to the next.
Recall in the lesson videos "Attention Model", at time 6:45 to 8:16, the definition of "e" as a function of \(s^{\langle t-1 \rangle}\) and \(a^{\langle t \rangle}\).
The diagram on the right of figure 1 uses a RepeatVector node to copy \(s^{\langle t-1 \rangle}\)'s value \(T_x\) times.
Then it uses Concatenation to concatenate \(s^{\langle t-1 \rangle}\) and \(a^{\langle t \rangle}\).
The concatenation of \(s^{\langle t-1 \rangle}\) and \(a^{\langle t \rangle}\) is fed into a "Dense" layer, which computes \(e^{\langle t, t' \rangle}\).
\(e^{\langle t, t' \rangle}\) is then passed through a softmax to compute \(\alpha^{\langle t, t' \rangle}\).
Note that the diagram doesn't explicitly show variable \(e^{\langle t, t' \rangle}\), but \(e^{\langle t, t' \rangle}\) is above the Dense layer and below the Softmax layer in the diagram in the right half of figure 1.
We'll explain how to use RepeatVector and Concatenation in Keras below.
Let's implement this neural translator. You will start by implementing two functions: one_step_attention() and model().
\[context^{<t>} = \sum_{t' = 1}^{T_x} \alpha^{<t,t'>}a^{<t'>}\tag{1}\]
one_step_attentionExercise: Implement one_step_attention().
model() will call the layers in one_step_attention() \(T_y\) using a for-loop.one_step_attention function. For example, defining the objects as global variables would work.
model would technically work, since model will then call the one_step_attention function. For the purposes of making grading and troubleshooting easier, we are defining these as global variables. Note that the automatic grader will expect these to be global variables as well.Python
var_repeated = repeat_layer(var1)
Python
concatenated_vars = concatenate_layer([var1,var2,var3])
Python
var_out = dense_layer(var_in)
Python
activation = activation_layer(var_in)
Python
dot_product = dot_layer([var1,var2])
# Defined shared layers as global variables
repeator = RepeatVector(Tx)
concatenator = Concatenate(axis=-1)
densor1 = Dense(10, activation = "tanh")
densor2 = Dense(1, activation = "relu")
activator = Activation(softmax, name='attention_weights') # We are using a custom softmax(axis = 1) loaded in this notebook
dotor = Dot(axes = 1)
# GRADED FUNCTION: one_step_attention
def one_step_attention(a, s_prev):
"""
Performs one step of attention: Outputs a context vector computed as a dot product of the attention weights
"alphas" and the hidden states "a" of the Bi-LSTM.
Arguments:
a -- hidden state output of the Bi-LSTM, numpy-array of shape (m, Tx, 2*n_a)
s_prev -- previous hidden state of the (post-attention) LSTM, numpy-array of shape (m, n_s)
Returns:
context -- context vector, input of the next (post-attention) LSTM cell
"""
### START CODE HERE ###
# Use repeator to repeat s_prev to be of shape (m, Tx, n_s) so that you can concatenate it with all hidden states "a" (≈ 1 line)
s_prev = repeator(s_prev)
# Use concatenator to concatenate a and s_prev on the last axis (≈ 1 line)
# For grading purposes, please list 'a' first and 's_prev' second, in this order.
concat = concatenator([a, s_prev])
# Use densor1 to propagate concat through a small fully-connected neural network to compute the "intermediate energies" variable e. (≈1 lines)
e = densor1(concat)
# Use densor2 to propagate e through a small fully-connected neural network to compute the "energies" variable energies. (≈1 lines)
energies = densor2(e)
# Use "activator" on "energies" to compute the attention weights "alphas" (≈ 1 line)
alphas = activator(energies)
# Use dotor together with "alphas" and "a" to compute the context vector to be given to the next (post-attention) LSTM-cell (≈ 1 line)
context = dotor([alphas, a])
### END CODE HERE ###
return context
You will be able to check the expected output of one_step_attention() after you've coded the model() function.
model first runs the input through a Bi-LSTM to get \([a^{<1>},a^{<2>}, ..., a^{<T_x>}]\). model calls one_step_attention() \(T_y\) times using a for loop. At each iteration of this loop:
Exercise: Implement model() as explained in figure 1 and the text above. Again, we have defined global layers that will share weights to be used in model().
n_a = 32 # number of units for the pre-attention, bi-directional LSTM's hidden state 'a'
n_s = 64 # number of units for the post-attention LSTM's hidden state "s"
# Please note, this is the post attention LSTM cell.
# For the purposes of passing the automatic grader
# please do not modify this global variable. This will be corrected once the automatic grader is also updated.
post_activation_LSTM_cell = LSTM(n_s, return_state = True) # post-attention LSTM
output_layer = Dense(len(machine_vocab), activation=softmax)
Now you can use these layers \(T_y\) times in a for loop to generate the outputs, and their parameters will not be reinitialized. You will have to carry out the following steps:
X into a bi-directional LSTM.
Sample code:
sequence_of_hidden_states = Bidirectional(LSTM(units=..., return_sequences=...))(the_input_X)
Iterate for \(t = 0, \cdots, T_y-1\):
one_step_attention(), passing in the sequence of hidden states \([a^{\langle 1 \rangle},a^{\langle 2 \rangle}, ..., a^{ \langle T_x \rangle}]\) from the pre-attention bi-directional LSTM, and the previous hidden state \(s^{<t-1>}\) from the post-attention LSTM to calculate the context vector \(context^{<t>}\).Give \(context^{<t>}\) to the post-attention LSTM cell.
Sample code:
next_hidden_state, _ , next_cell_state =
post_activation_LSTM_cell(inputs=..., initial_state=[prev_hidden_state, prev_cell_state])
Please note that the layer is actually the "post attention LSTM cell". For the purposes of passing the automatic grader, please do not modify the naming of this global variable. This will be fixed when we deploy updates to the automatic grader.
Apply a dense, softmax layer to \(s^{<t>}\), get the output.
Sample code:
output = output_layer(inputs=...)
Save the output by adding it to the list of outputs.
Create your Keras model instance.
X, the one-hot encoded inputs to the model, of shape (\(T_{x}, humanVocabSize)\)Python
model = Model(inputs=[...,...,...], outputs=...)
# GRADED FUNCTION: model
def model(Tx, Ty, n_a, n_s, human_vocab_size, machine_vocab_size):
"""
Arguments:
Tx -- length of the input sequence
Ty -- length of the output sequence
n_a -- hidden state size of the Bi-LSTM
n_s -- hidden state size of the post-attention LSTM
human_vocab_size -- size of the python dictionary "human_vocab"
machine_vocab_size -- size of the python dictionary "machine_vocab"
Returns:
model -- Keras model instance
"""
# Define the inputs of your model with a shape (Tx,)
# Define s0 (initial hidden state) and c0 (initial cell state)
# for the decoder LSTM with shape (n_s,)
X = Input(shape=(Tx, human_vocab_size))
s0 = Input(shape=(n_s,), name='s0')
c0 = Input(shape=(n_s,), name='c0')
s = s0
c = c0
# Initialize empty list of outputs
outputs = []
### START CODE HERE ###
# Step 1: Define your pre-attention Bi-LSTM. Remember to use return_sequences=True. (≈ 1 line)
a = Bidirectional(LSTM(n_a, return_sequences=True),input_shape=(m, Tx, n_a*2))(X)
print(a.shape)
print(Ty)
# Step 2: Iterate for Ty steps
for t in range(Ty):
# Step 2.A: Perform one step of the attention mechanism to get back the context vector at step t (≈ 1 line)
context = one_step_attention(a, s)
# Step 2.B: Apply the post-attention LSTM cell to the "context" vector.
# Don't forget to pass: initial_state = [hidden state, cell state] (≈ 1 line)
s, _, c = post_activation_LSTM_cell(context , initial_state=[s, c])
# Step 2.C: Apply Dense layer to the hidden state output of the post-attention LSTM (≈ 1 line)
out = output_layer(s)
# Step 2.D: Append "out" to the "outputs" list (≈ 1 line)
outputs.append(out)
# Step 3: Create model instance taking three inputs and returning the list of outputs. (≈ 1 line)
model = Model(inputs=[X, s0, c0], outputs = outputs)
### END CODE HERE ###
return model
Run the following cell to create your model.
model = model(Tx, Ty, n_a, n_s, len(human_vocab), len(machine_vocab))
(?, ?, 64)
10
Let's get a summary of the model to check if it matches the expected output.
model.summary()
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 30, 37) 0
____________________________________________________________________________________________________
s0 (InputLayer) (None, 64) 0
____________________________________________________________________________________________________
bidirectional_1 (Bidirectional) (None, 30, 64) 17920 input_1[0][0]
____________________________________________________________________________________________________
repeat_vector_1 (RepeatVector) (None, 30, 64) 0 s0[0][0]
lstm_1[0][0]
lstm_1[1][0]
lstm_1[2][0]
lstm_1[3][0]
lstm_1[4][0]
lstm_1[5][0]
lstm_1[6][0]
lstm_1[7][0]
lstm_1[8][0]
____________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 30, 128) 0 bidirectional_1[0][0]
repeat_vector_1[0][0]
bidirectional_1[0][0]
repeat_vector_1[1][0]
bidirectional_1[0][0]
repeat_vector_1[2][0]
bidirectional_1[0][0]
repeat_vector_1[3][0]
bidirectional_1[0][0]
repeat_vector_1[4][0]
bidirectional_1[0][0]
repeat_vector_1[5][0]
bidirectional_1[0][0]
repeat_vector_1[6][0]
bidirectional_1[0][0]
repeat_vector_1[7][0]
bidirectional_1[0][0]
repeat_vector_1[8][0]
bidirectional_1[0][0]
repeat_vector_1[9][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 30, 10) 1290 concatenate_1[0][0]
concatenate_1[1][0]
concatenate_1[2][0]
concatenate_1[3][0]
concatenate_1[4][0]
concatenate_1[5][0]
concatenate_1[6][0]
concatenate_1[7][0]
concatenate_1[8][0]
concatenate_1[9][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 30, 1) 11 dense_1[0][0]
dense_1[1][0]
dense_1[2][0]
dense_1[3][0]
dense_1[4][0]
dense_1[5][0]
dense_1[6][0]
dense_1[7][0]
dense_1[8][0]
dense_1[9][0]
____________________________________________________________________________________________________
attention_weights (Activation) (None, 30, 1) 0 dense_2[0][0]
dense_2[1][0]
dense_2[2][0]
dense_2[3][0]
dense_2[4][0]
dense_2[5][0]
dense_2[6][0]
dense_2[7][0]
dense_2[8][0]
dense_2[9][0]
____________________________________________________________________________________________________
dot_1 (Dot) (None, 1, 64) 0 attention_weights[0][0]
bidirectional_1[0][0]
attention_weights[1][0]
bidirectional_1[0][0]
attention_weights[2][0]
bidirectional_1[0][0]
attention_weights[3][0]
bidirectional_1[0][0]
attention_weights[4][0]
bidirectional_1[0][0]
attention_weights[5][0]
bidirectional_1[0][0]
attention_weights[6][0]
bidirectional_1[0][0]
attention_weights[7][0]
bidirectional_1[0][0]
attention_weights[8][0]
bidirectional_1[0][0]
attention_weights[9][0]
bidirectional_1[0][0]
____________________________________________________________________________________________________
c0 (InputLayer) (None, 64) 0
____________________________________________________________________________________________________
lstm_1 (LSTM) [(None, 64), (None, 6 33024 dot_1[0][0]
s0[0][0]
c0[0][0]
dot_1[1][0]
lstm_1[0][0]
lstm_1[0][2]
dot_1[2][0]
lstm_1[1][0]
lstm_1[1][2]
dot_1[3][0]
lstm_1[2][0]
lstm_1[2][2]
dot_1[4][0]
lstm_1[3][0]
lstm_1[3][2]
dot_1[5][0]
lstm_1[4][0]
lstm_1[4][2]
dot_1[6][0]
lstm_1[5][0]
lstm_1[5][2]
dot_1[7][0]
lstm_1[6][0]
lstm_1[6][2]
dot_1[8][0]
lstm_1[7][0]
lstm_1[7][2]
dot_1[9][0]
lstm_1[8][0]
lstm_1[8][2]
____________________________________________________________________________________________________
dense_3 (Dense) (None, 11) 715 lstm_1[0][0]
lstm_1[1][0]
lstm_1[2][0]
lstm_1[3][0]
lstm_1[4][0]
lstm_1[5][0]
lstm_1[6][0]
lstm_1[7][0]
lstm_1[8][0]
lstm_1[9][0]
====================================================================================================
Total params: 52,960
Trainable params: 52,960
Non-trainable params: 0
____________________________________________________________________________________________________
Expected Output:
Here is the summary you should see
Total params: | 52,960 |
Trainable params: | 52,960 |
Non-trainable params: | 0 |
**bidirectional_1's output shape ** | (None, 30, 64) |
**repeat_vector_1's output shape ** | (None, 30, 64) |
**concatenate_1's output shape ** | (None, 30, 128) |
**attention_weights's output shape ** | (None, 30, 1) |
**dot_1's output shape ** | (None, 1, 64) |
**dense_3's output shape ** | (None, 11) |
Sample code
optimizer = Adam(lr=..., beta_1=..., beta_2=..., decay=...)
model.compile(optimizer=..., loss=..., metrics=[...])
### START CODE HERE ### (≈2 lines)
opt = Adam(lr = 0.005, beta_1=0.9, beta_2=0.999, decay=0.01)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
### END CODE HERE ###
The last step is to define all your inputs and outputs to fit the model:
s0 and c0 to initialize your post_attention_LSTM_cell with zeros.model() you coded, you need the "outputs" to be a list of 10 elements of shape (m, T_y).
outputs[i][0], ..., outputs[i][Ty] represents the true labels (characters) corresponding to the \(i^{th}\) training example (X[i]). outputs[i][j] is the true label of the \(j^{th}\) character in the \(i^{th}\) training example.s0 = np.zeros((m, n_s))
c0 = np.zeros((m, n_s))
outputs = list(Yoh.swapaxes(0,1))
Let's now fit the model and run it for one epoch.
model.fit([Xoh, s0, c0], outputs, epochs=1, batch_size=100)
Epoch 1/1
10000/10000 [==============================] - 56s - loss: 17.1337 - dense_3_loss_1: 1.1702 - dense_3_loss_2: 1.0040 - dense_3_loss_3: 1.7835 - dense_3_loss_4: 2.7342 - dense_3_loss_5: 0.8600 - dense_3_loss_6: 1.4397 - dense_3_loss_7: 2.7599 - dense_3_loss_8: 1.0377 - dense_3_loss_9: 1.7478 - dense_3_loss_10: 2.5966 - dense_3_acc_1: 0.5124 - dense_3_acc_2: 0.6774 - dense_3_acc_3: 0.2909 - dense_3_acc_4: 0.0719 - dense_3_acc_5: 0.9503 - dense_3_acc_6: 0.2390 - dense_3_acc_7: 0.0278 - dense_3_acc_8: 0.9483 - dense_3_acc_9: 0.2304 - dense_3_acc_10: 0.0953
<keras.callbacks.History at 0x7f5bdc160ef0>
While training you can see the loss as well as the accuracy on each of the 10 positions of the output. The table below gives you an example of what the accuracies could be if the batch had 2 examples:
style="width:700;height:200px;">
dense_2_acc_8: 0.89 means that you are predicting the 7th character of the output correctly 89% of the time in the current batch of data. We have run this model for longer, and saved the weights. Run the next cell to load our weights. (By training a model for several minutes, you should be able to obtain a model of similar accuracy, but loading our model will save you time.)
model.load_weights('models/model.h5')
You can now see the results on new examples.
EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']
for example in EXAMPLES:
source = string_to_int(example, Tx, human_vocab)
source = np.array(list(map(lambda x: to_categorical(x, num_classes=len(human_vocab)), source))).swapaxes(0,1)
prediction = model.predict([source, s0, c0])
prediction = np.argmax(prediction, axis = -1)
output = [inv_machine_vocab[int(i)] for i in prediction]
print("source:", example)
print("output:", ''.join(output),"\n")
source: 3 May 1979
output: 1979-05-03
source: 5 April 09
output: 2009-05-05
source: 21th of August 2016
output: 2016-08-21
source: Tue 10 Jul 2007
output: 2007-07-10
source: Saturday May 9 2018
output: 2018-05-09
source: March 3 2001
output: 2001-03-03
source: March 3rd 2001
output: 2001-03-03
source: 1 March 2001
output: 2001-03-01
You can also change these examples to test with your own examples. The next part will give you a better sense of what the attention mechanism is doing--i.e., what part of the input the network is paying attention to when generating a particular output character.
Since the problem has a fixed output length of 10, it is also possible to carry out this task using 10 different softmax units to generate the 10 characters of the output. But one advantage of the attention model is that each part of the output (such as the month) knows it needs to depend only on a small part of the input (the characters in the input giving the month). We can visualize what each part of the output is looking at which part of the input.
Consider the task of translating "Saturday 9 May 2018" to "2018-05-09". If we visualize the computed \(\alpha^{\langle t, t' \rangle}\) we get this:
style="width:600;height:300px;">
Notice how the output ignores the "Saturday" portion of the input. None of the output timesteps are paying much attention to that portion of the input. We also see that 9 has been translated as 09 and May has been correctly translated into 05, with the output paying attention to the parts of the input it needs to to make the translation. The year mostly requires it to pay attention to the input's "18" in order to generate "2018."
Lets now visualize the attention values in your network. We'll propagate an example through the network, then visualize the values of \(\alpha^{\langle t, t' \rangle}\).
To figure out where the attention values are located, let's start by printing a summary of the model .
model.summary()
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 30, 37) 0
____________________________________________________________________________________________________
s0 (InputLayer) (None, 64) 0
____________________________________________________________________________________________________
bidirectional_1 (Bidirectional) (None, 30, 64) 17920 input_1[0][0]
____________________________________________________________________________________________________
repeat_vector_1 (RepeatVector) (None, 30, 64) 0 s0[0][0]
lstm_1[0][0]
lstm_1[1][0]
lstm_1[2][0]
lstm_1[3][0]
lstm_1[4][0]
lstm_1[5][0]
lstm_1[6][0]
lstm_1[7][0]
lstm_1[8][0]
____________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 30, 128) 0 bidirectional_1[0][0]
repeat_vector_1[0][0]
bidirectional_1[0][0]
repeat_vector_1[1][0]
bidirectional_1[0][0]
repeat_vector_1[2][0]
bidirectional_1[0][0]
repeat_vector_1[3][0]
bidirectional_1[0][0]
repeat_vector_1[4][0]
bidirectional_1[0][0]
repeat_vector_1[5][0]
bidirectional_1[0][0]
repeat_vector_1[6][0]
bidirectional_1[0][0]
repeat_vector_1[7][0]
bidirectional_1[0][0]
repeat_vector_1[8][0]
bidirectional_1[0][0]
repeat_vector_1[9][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 30, 10) 1290 concatenate_1[0][0]
concatenate_1[1][0]
concatenate_1[2][0]
concatenate_1[3][0]
concatenate_1[4][0]
concatenate_1[5][0]
concatenate_1[6][0]
concatenate_1[7][0]
concatenate_1[8][0]
concatenate_1[9][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 30, 1) 11 dense_1[0][0]
dense_1[1][0]
dense_1[2][0]
dense_1[3][0]
dense_1[4][0]
dense_1[5][0]
dense_1[6][0]
dense_1[7][0]
dense_1[8][0]
dense_1[9][0]
____________________________________________________________________________________________________
attention_weights (Activation) (None, 30, 1) 0 dense_2[0][0]
dense_2[1][0]
dense_2[2][0]
dense_2[3][0]
dense_2[4][0]
dense_2[5][0]
dense_2[6][0]
dense_2[7][0]
dense_2[8][0]
dense_2[9][0]
____________________________________________________________________________________________________
dot_1 (Dot) (None, 1, 64) 0 attention_weights[0][0]
bidirectional_1[0][0]
attention_weights[1][0]
bidirectional_1[0][0]
attention_weights[2][0]
bidirectional_1[0][0]
attention_weights[3][0]
bidirectional_1[0][0]
attention_weights[4][0]
bidirectional_1[0][0]
attention_weights[5][0]
bidirectional_1[0][0]
attention_weights[6][0]
bidirectional_1[0][0]
attention_weights[7][0]
bidirectional_1[0][0]
attention_weights[8][0]
bidirectional_1[0][0]
attention_weights[9][0]
bidirectional_1[0][0]
____________________________________________________________________________________________________
c0 (InputLayer) (None, 64) 0
____________________________________________________________________________________________________
lstm_1 (LSTM) [(None, 64), (None, 6 33024 dot_1[0][0]
s0[0][0]
c0[0][0]
dot_1[1][0]
lstm_1[0][0]
lstm_1[0][2]
dot_1[2][0]
lstm_1[1][0]
lstm_1[1][2]
dot_1[3][0]
lstm_1[2][0]
lstm_1[2][2]
dot_1[4][0]
lstm_1[3][0]
lstm_1[3][2]
dot_1[5][0]
lstm_1[4][0]
lstm_1[4][2]
dot_1[6][0]
lstm_1[5][0]
lstm_1[5][2]
dot_1[7][0]
lstm_1[6][0]
lstm_1[6][2]
dot_1[8][0]
lstm_1[7][0]
lstm_1[7][2]
dot_1[9][0]
lstm_1[8][0]
lstm_1[8][2]
____________________________________________________________________________________________________
dense_3 (Dense) (None, 11) 715 lstm_1[0][0]
lstm_1[1][0]
lstm_1[2][0]
lstm_1[3][0]
lstm_1[4][0]
lstm_1[5][0]
lstm_1[6][0]
lstm_1[7][0]
lstm_1[8][0]
lstm_1[9][0]
====================================================================================================
Total params: 52,960
Trainable params: 52,960
Non-trainable params: 0
____________________________________________________________________________________________________
Navigate through the output of model.summary() above. You can see that the layer named attention_weights outputs the alphas of shape (m, 30, 1) before dot_2 computes the context vector for every time step \(t = 0, \ldots, T_y-1\). Let's get the attention weights from this layer.
The function attention_map() pulls out the attention values from your model and plots them.
attention_map = plot_attention_map(model, human_vocab, inv_machine_vocab, "Tuesday 09 Oct 1993", num = 7, n_s = 64);
<matplotlib.figure.Figure at 0x7f5bb47f4da0>
On the generated plot you can observe the values of the attention weights for each character of the predicted output. Examine this plot and check that the places where the network is paying attention makes sense to you.
In the date translation application, you will observe that most of the time attention helps predict the year, and doesn't have much impact on predicting the day or month.
You have come to the end of this assignment
Congratulations on finishing this assignment! You are now able to implement an attention model and use it to learn complex mappings from one sequence to another.
Neural_machine_translation_with_attention_v4a
标签：label more complex different written app tom new t sof
原文地址：https://www.cnblogs.com/tolshao/p/neuralmachinetranslationwithattentionv4a.html
