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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:
 Language Translation
In this project, you’re going to take a peek into the realm of neural network machine translation.  You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.
Get the Data
Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus.
Step1:  Explore the Data
Play around with view_sentence_range to view different parts of the data.
Step3:  Implement Preprocessing Function
Text to Word Ids
As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function text_to_ids(), you'll turn source_text and target_text from words to ids.  However, you need to add the &lt;EOS&gt; word id at the end of target_text.  This will help the neural network predict when the sentence should end.
You can get the &lt;EOS&gt; word id by doing
Step4:  Preprocess all the data and save it
Running the code cell below will preprocess all the data and save it to file.
Step5:  Check Point
This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.
Step6:  Check the Version of TensorFlow and Access to GPU
This will check to make sure you have the correct version of TensorFlow and access to a GPU
Step8:  Build the Neural Network
You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below
Step10:  Process Decoder Input
Implement process_decoder_input by removing the last word id from each batch in target_data and concat the GO ID to the begining of each batch.
Step12:  Encoding
Implement encoding_layer() to create a Encoder RNN layer
Step14:  Decoding - Training
Create a training decoding layer
Step16:  Decoding - Inference
Create inference decoder
Step18:  Build the Decoding Layer
Implement decoding_layer() to create a Decoder RNN layer.
Embed the target sequences
Construct the decoder LSTM cell (just like you constructed the encoder cell above)
Create an output layer to map the outputs of the decoder to the elements of our vocabulary
Use the your decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_target_sequence_length, output_layer, keep_prob) function to get the training logits.
Use your decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob) function to get the inference logits.
Note
Step20:  Build the Neural Network
Apply the functions you implemented above to
Step21:  Neural Network Training
Hyperparameters
Tune the following parameters
Step22:  Build the Graph
Build the graph using the neural network you implemented.
Step25:  Batch and pad the source and target sequences
Step27:  Train
Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem.
Step28:  My conclusions from hyperparameter adjustmensts are that for comparable results
Step29:  Checkpoint
Step31:  Sentence to Sequence
To feed a sentence into the model for translation, you first need to preprocess it.  Implement the function sentence_to_seq() to preprocess new sentences.
Convert the sentence to lowercase
Convert words into ids using vocab_to_int
Convert words not in the vocabulary, to the &lt;UNK&gt; word id.
Step32:  Translate
This will translate translate_sentence from English to French.
Step33:  Imperfect Translation
You might notice that some sentences translate better than others.  Since the dataset you're using only has a vocabulary of 227 English words of the thousands that you use, you're only going to see good results using these words.  For this project, you don't need a perfect translation. However, if you want to create a better translation model, you'll need better data.
You can train on the WMT10 French-English corpus.  This dataset has more vocabulary and richer in topics discussed.  However, this will take you days to train, so make sure you've a GPU and the neural network is performing well on dataset we provided.  Just make sure you play with the WMT10 corpus after you've submitted this project.
Submitting This Project
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_language_translation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.
Appendix
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Python Code:
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
Explanation: Language Translation
In this project, you’re going to take a peek into the realm of neural network machine translation.  You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.
Get the Data
Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus.
End of explanation
view_sentence_range = (0, 10)
import numpy as np
print('Dataset Stats')
print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))
sentences = source_text.split('\n')
word_counts = [len(sentence.split()) for sentence in sentences]
print('Number of sentences: {}'.format(len(sentences)))
print('Average number of words in a sentence: {}'.format(np.average(word_counts)))
print()
print('English sentences {} to {}:'.format(*view_sentence_range))
print('\n'.join(source_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
print()
print('French sentences {} to {}:'.format(*view_sentence_range))
print('\n'.join(target_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
Explanation: Explore the Data
Play around with view_sentence_range to view different parts of the data.
End of explanation
def text_to_ids(source_text, target_text, source_vocab_to_int,
                target_vocab_to_int):
    
    Convert source and target text to proper vectors of word ids
    :param source_text: String that contains all the source text.
    :param target_text: String that contains all the target text.
    :param source_vocab_to_int: Dictionary to go from the source words to an id
    :param target_vocab_to_int: Dictionary to go from the target words to an id
    :return: A tuple of lists of lists (source_id_text, target_id_text)
    
    sentences = source_text.split('\n')
    source_vectors = []
    for sent in sentences:
        source_vectors.append([
            source_vocab_to_int[word] for word in sent.split(' ') if word != ''
        ])
    sentences = target_text.split('\n')
    target_vectors = []
    for sent in sentences:
        target_vectors.append([
            target_vocab_to_int[word] for word in sent.split(' ') if word != ''
        ] + [target_vocab_to_int['<EOS>']])
    return source_vectors, target_vectors
tests.test_text_to_ids(text_to_ids)
Explanation: Implement Preprocessing Function
Text to Word Ids
As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function text_to_ids(), you'll turn source_text and target_text from words to ids.  However, you need to add the &lt;EOS&gt; word id at the end of target_text.  This will help the neural network predict when the sentence should end.
You can get the &lt;EOS&gt; word id by doing:
python
target_vocab_to_int['&lt;EOS&gt;']
You can get other word ids using source_vocab_to_int and target_vocab_to_int.
End of explanation
helper.preprocess_and_save_data(source_path, target_path, text_to_ids)
Explanation: Preprocess all the data and save it
Running the code cell below will preprocess all the data and save it to file.
End of explanation
import numpy as np
import helper
import problem_unittests as tests
(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()
Explanation: Check Point
This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.
End of explanation
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
from tensorflow.python.layers.core import Dense
# Check TensorFlow Version
assert LooseVersion(tf.__version__) == LooseVersion('1.1.0'), 'Please use TensorFlow version 1.1'
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Explanation: Check the Version of TensorFlow and Access to GPU
This will check to make sure you have the correct version of TensorFlow and access to a GPU
End of explanation
def model_inputs():
    
    Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences.
    :return: Tuple (input, targets, learning rate, keep probability, target sequence length,
    max target sequence length, source sequence length)
    
    inputs = tf.placeholder(tf.int32, [None, None], 'input')
    targets = tf.placeholder(tf.int32, [None, None], 'target')
    lr = tf.placeholder(tf.float32, name='lr')
    kp = tf.placeholder(tf.float32, name='keep_prob')
    target_seq_len = tf.placeholder(tf.int32, [None], name='target_sequence_length')
    max_target_seq_len = tf.reduce_max(target_seq_len, name='max_target_len')
    source_seq_len = tf.placeholder(tf.int32, [None], name='source_sequence_length')
    return inputs, targets, lr, kp, target_seq_len, max_target_seq_len, source_seq_len
tests.test_model_inputs(model_inputs)
Explanation: Build the Neural Network
You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below:
- model_inputs
- process_decoder_input
- encoding_layer
- decoding_layer_train
- decoding_layer_infer
- decoding_layer
- seq2seq_model
Input
Implement the model_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders:
Input text placeholder named "input" using the TF Placeholder name parameter with rank 2.
Targets placeholder with rank 2.
Learning rate placeholder with rank 0.
Keep probability placeholder named "keep_prob" using the TF Placeholder name parameter with rank 0.
Target sequence length placeholder named "target_sequence_length" with rank 1
Max target sequence length tensor named "max_target_len" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0.
Source sequence length placeholder named "source_sequence_length" with rank 1
End of explanation
def process_decoder_input(target_data, target_vocab_to_int, batch_size):
    
    Preprocess target data for encoding
    :param target_data: Target Placehoder
    :param target_vocab_to_int: Dictionary to go from the target words to an id
    :param batch_size: Batch Size
    :return: Preprocessed target data
    
    # difficult way
    # ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])
    # easy way
    ending = target_data[:, :-1]
    dec_input = tf.concat(
        values=[tf.fill(dims=[batch_size, 1], value=target_vocab_to_int['<GO>']),
                ending],
        axis=1)
    return dec_input
tests.test_process_encoding_input(process_decoder_input)
Explanation: Process Decoder Input
Implement process_decoder_input by removing the last word id from each batch in target_data and concat the GO ID to the begining of each batch.
End of explanation
from imp import reload
reload(tests)
def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob,
                   source_sequence_length, source_vocab_size,
                   encoding_embedding_size):
    
    Create encoding layer
    :param rnn_inputs: Inputs for the RNN
    :param rnn_size: RNN Size
    :param num_layers: Number of layers
    :param keep_prob: Dropout keep probability
    :param source_sequence_length: a list of the lengths of each sequence in the batch
    :param source_vocab_size: vocabulary size of source data
    :param encoding_embedding_size: embedding size of source data
    :return: tuple (RNN output, RNN state)
    
    emb = tf.contrib.layers.embed_sequence(
        ids=rnn_inputs,
        vocab_size=source_vocab_size,
        embed_dim=encoding_embedding_size)
    def wrapped_cell(rnn_size, keep_prob):
        initer = tf.random_uniform_initializer(-0.1, 0.1, seed=2)
        cell = tf.contrib.rnn.LSTMCell(num_units=rnn_size, initializer=initer)
        return tf.contrib.rnn.DropoutWrapper(
            cell=cell, input_keep_prob=keep_prob)
    stacked = tf.contrib.rnn.MultiRNNCell(
        [wrapped_cell(rnn_size, keep_prob) for _ in range(num_layers)])
    rnn_output, rnn_state = tf.nn.dynamic_rnn(
        cell=stacked,
        inputs=emb,
        sequence_length=source_sequence_length,
        dtype=tf.float32)
    return rnn_output, rnn_state
tests.test_encoding_layer(encoding_layer)
Explanation: Encoding
Implement encoding_layer() to create a Encoder RNN layer:
 * Embed the encoder input using tf.contrib.layers.embed_sequence
 * Construct a stacked tf.contrib.rnn.LSTMCell wrapped in a tf.contrib.rnn.DropoutWrapper
 * Pass cell and embedded input to tf.nn.dynamic_rnn()
End of explanation
def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, 
                         target_sequence_length, max_target_sequence_length, 
                         output_layer, keep_prob):
    
    Create a decoding layer for training
    :param encoder_state: Encoder State
    :param dec_cell: Decoder RNN Cell
    :param dec_embed_input: Decoder embedded input
    :param target_sequence_length: The lengths of each sequence in the target batch
    :param max_summary_length: The length of the longest sequence in the batch
    :param output_layer: Function to apply the output layer
    :param keep_prob: Dropout keep probability
    :return: BasicDecoder output containing training logits and sample_id
    
    train_help = tf.contrib.seq2seq.TrainingHelper(inputs=dec_embed_input, 
                                                   sequence_length=target_sequence_length)
    
    train_decoder = tf.contrib.seq2seq.BasicDecoder(cell=dec_cell, 
                                                    helper=train_help,
                                                    initial_state=encoder_state,
                                                    output_layer=output_layer)
    
    f_outputs, f_state = tf.contrib.seq2seq.dynamic_decode(decoder=train_decoder,
                                                           impute_finished=True, 
                                                           maximum_iterations=max_target_sequence_length)
    
    # TODO: keep_prob - unused argument?
    return f_outputs
tests.test_decoding_layer_train(decoding_layer_train)
Explanation: Decoding - Training
Create a training decoding layer:
* Create a tf.contrib.seq2seq.TrainingHelper 
* Create a tf.contrib.seq2seq.BasicDecoder
* Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode
End of explanation
def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id,
                         end_of_sequence_id, max_target_sequence_length,
                         vocab_size, output_layer, batch_size, keep_prob):
    
    Create a decoding layer for inference
    :param encoder_state: Encoder state
    :param dec_cell: Decoder RNN Cell
    :param dec_embeddings: Decoder embeddings
    :param start_of_sequence_id: GO ID
    :param end_of_sequence_id: EOS Id
    :param max_target_sequence_length: Maximum length of target sequences
    :param vocab_size: Size of decoder/target vocabulary
    :param output_layer: Function to apply the output layer
    :param batch_size: Batch size
    :param keep_prob: Dropout keep probability
    :return: BasicDecoder output containing inference logits and sample_id
    
    start_tokens = tf.tile(input=tf.constant([start_of_sequence_id], dtype=tf.int32),
                           multiples=[batch_size],
                           name='start_tokens')
    infer_help = tf.contrib.seq2seq.GreedyEmbeddingHelper(embedding=dec_embeddings, 
                                                          start_tokens=start_tokens, 
                                                          end_token=end_of_sequence_id)
    infer_decoder = tf.contrib.seq2seq.BasicDecoder(cell=dec_cell, 
                                                    helper=infer_help,
                                                    initial_state=encoder_state,
                                                    output_layer=output_layer)
    f_outputs, f_state = tf.contrib.seq2seq.dynamic_decode(decoder=infer_decoder,
                                                           impute_finished=True, 
                                                           maximum_iterations=max_target_sequence_length)
    
    # TODO: keep_prob - unused argument
    return f_outputs
tests.test_decoding_layer_infer(decoding_layer_infer)
Explanation: Decoding - Inference
Create inference decoder:
* Create a tf.contrib.seq2seq.GreedyEmbeddingHelper
* Create a tf.contrib.seq2seq.BasicDecoder
* Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode
End of explanation
def decoding_layer(dec_input, encoder_state, target_sequence_length,
                   max_target_sequence_length, rnn_size, num_layers,
                   target_vocab_to_int, target_vocab_size, batch_size,
                   keep_prob, decoding_embedding_size):
    
    Create decoding layer
    :param dec_input: Decoder input
    :param encoder_state: Encoder state
    :param target_sequence_length: The lengths of each sequence in the target batch
    :param max_target_sequence_length: Maximum length of target sequences
    :param rnn_size: RNN Size
    :param num_layers: Number of layers
    :param target_vocab_to_int: Dictionary to go from the target words to an id
    :param target_vocab_size: Size of target vocabulary
    :param batch_size: The size of the batch
    :param keep_prob: Dropout keep probability
    :param decoding_embedding_size: Decoding embedding size
    :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)
    
    # Embed the target sequences
    dec_embeddings = tf.Variable(
        tf.random_uniform([target_vocab_size, decoding_embedding_size]))
    emb = tf.nn.embedding_lookup(dec_embeddings, dec_input)
    # Construct the decoder LSTM cell 
    #(just like you constructed the encoder cell above)
    def make_cell(rnn_size):
        dec_cell = tf.contrib.rnn.LSTMCell(
            rnn_size,
            initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))
        return dec_cell
    dec_cell = tf.contrib.rnn.MultiRNNCell(
        [make_cell(rnn_size) for _ in range(num_layers)])
    # Create an output layer to map the outputs of the decoder 
    # to the elements of our vocabulary
    output_layer = Dense(
        target_vocab_size,
        kernel_initializer=tf.truncated_normal_initializer(
            mean=0.0, stddev=0.1))
    with tf.variable_scope("decode"):
        train_logits = decoding_layer_train(
            encoder_state, dec_cell, emb, target_sequence_length,
            max_target_sequence_length, output_layer, keep_prob)
    with tf.variable_scope("decode", reuse=True):
        infer_logits = decoding_layer_infer(
            encoder_state, dec_cell, dec_embeddings,
            target_vocab_to_int['<GO>'], target_vocab_to_int['<EOS>'],
            max_target_sequence_length, target_vocab_size, output_layer,
            batch_size, keep_prob)
    return train_logits, infer_logits
tests.test_decoding_layer(decoding_layer)
Explanation: Build the Decoding Layer
Implement decoding_layer() to create a Decoder RNN layer.
Embed the target sequences
Construct the decoder LSTM cell (just like you constructed the encoder cell above)
Create an output layer to map the outputs of the decoder to the elements of our vocabulary
Use the your decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_target_sequence_length, output_layer, keep_prob) function to get the training logits.
Use your decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob) function to get the inference logits.
Note: You'll need to use tf.variable_scope to share variables between training and inference.
End of explanation
def seq2seq_model(input_data, target_data, keep_prob, batch_size,
                  source_sequence_length, target_sequence_length,
                  max_target_sentence_length, source_vocab_size,
                  target_vocab_size, enc_embedding_size, dec_embedding_size,
                  rnn_size, num_layers, target_vocab_to_int):
    
    Build the Sequence-to-Sequence part of the neural network
    :param input_data: Input placeholder
    :param target_data: Target placeholder
    :param keep_prob: Dropout keep probability placeholder
    :param batch_size: Batch Size
    :param source_sequence_length: Sequence Lengths of source sequences in the batch
    :param target_sequence_length: Sequence Lengths of target sequences in the batch
    :param source_vocab_size: Source vocabulary size
    :param target_vocab_size: Target vocabulary size
    :param enc_embedding_size: Decoder embedding size
    :param dec_embedding_size: Encoder embedding size
    :param rnn_size: RNN Size
    :param num_layers: Number of layers
    :param target_vocab_to_int: Dictionary to go from the target words to an id
    :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)
    
    _, enc_state = encoding_layer(
        input_data, rnn_size, num_layers, keep_prob, source_sequence_length,
        source_vocab_size, enc_embedding_size)
    dec_input = process_decoder_input(target_data, target_vocab_to_int, batch_size)
    train_dec_out, infer_dec_out = decoding_layer(
        dec_input, enc_state, target_sequence_length,
        max_target_sentence_length, rnn_size, num_layers, target_vocab_to_int,
        target_vocab_size, batch_size, keep_prob, dec_embedding_size)
    return train_dec_out, infer_dec_out
tests.test_seq2seq_model(seq2seq_model)
Explanation: Build the Neural Network
Apply the functions you implemented above to:
Encode the input using your encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob,  source_sequence_length, source_vocab_size, encoding_embedding_size).
Process target data using your process_decoder_input(target_data, target_vocab_to_int, batch_size) function.
Decode the encoded input using your decoding_layer(dec_input, enc_state, target_sequence_length, max_target_sentence_length, rnn_size, num_layers, target_vocab_to_int, target_vocab_size, batch_size, keep_prob, dec_embedding_size) function.
End of explanation
# Number of Epochs
epochs = 20
# Batch Size
batch_size = 512
# RNN Size
rnn_size = 512
# Number of Layers
num_layers = 2
# Embedding Size
encoding_embedding_size = 64
decoding_embedding_size = 64
# Learning Rate
learning_rate = 0.001
# Dropout Keep Probability
keep_probability = 0.5
display_step = 50
Explanation: Neural Network Training
Hyperparameters
Tune the following parameters:
Set epochs to the number of epochs.
Set batch_size to the batch size.
Set rnn_size to the size of the RNNs.
Set num_layers to the number of layers.
Set encoding_embedding_size to the size of the embedding for the encoder.
Set decoding_embedding_size to the size of the embedding for the decoder.
Set learning_rate to the learning rate.
Set keep_probability to the Dropout keep probability
Set display_step to state how many steps between each debug output statement
End of explanation
save_path = 'checkpoints/dev'
((source_int_text, target_int_text),
 (source_vocab_to_int, target_vocab_to_int), _) = helper.load_preprocess()
max_target_sentence_length = max([len(sentence) for sentence in source_int_text])
train_graph = tf.Graph()
with train_graph.as_default():
    (input_data, targets, lr, keep_prob, target_sequence_length,
     max_target_sequence_length, source_sequence_length) = model_inputs()
    #sequence_length = tf.placeholder_with_default(max_target_sentence_length, None, name='sequence_length')
    input_shape = tf.shape(input_data)
    train_logits, inference_logits = seq2seq_model(tf.reverse(input_data, [-1]),
                                                   targets,
                                                   keep_prob,
                                                   batch_size,
                                                   source_sequence_length,
                                                   target_sequence_length,
                                                   max_target_sequence_length,
                                                   len(source_vocab_to_int),
                                                   len(target_vocab_to_int),
                                                   encoding_embedding_size,
                                                   decoding_embedding_size,
                                                   rnn_size,
                                                   num_layers,
                                                   target_vocab_to_int)
    training_logits = tf.identity(train_logits.rnn_output, name='logits')
    inference_logits = tf.identity(inference_logits.sample_id, name='predictions')
    masks = tf.sequence_mask(target_sequence_length, max_target_sequence_length, dtype=tf.float32, name='masks')
    with tf.name_scope("optimization"):
        # Loss function
        cost = tf.contrib.seq2seq.sequence_loss(
            training_logits,
            targets,
            masks)
        # Optimizer
        optimizer = tf.train.AdamOptimizer(lr)
        # Gradient Clipping
        gradients = optimizer.compute_gradients(cost)
        capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]
        train_op = optimizer.apply_gradients(capped_gradients)
Explanation: Build the Graph
Build the graph using the neural network you implemented.
End of explanation
def pad_sentence_batch(sentence_batch, pad_int):
    Pad sentences with <PAD> so that each sentence of a batch has the same length
    max_sentence = max([len(sentence) for sentence in sentence_batch])
    return [sentence + [pad_int] * (max_sentence - len(sentence)) for sentence in sentence_batch]
def get_batches(sources, targets, batch_size, source_pad_int, target_pad_int):
    Batch targets, sources, and the lengths of their sentences together
    for batch_i in range(0, len(sources)//batch_size):
        start_i = batch_i * batch_size
        # Slice the right amount for the batch
        sources_batch = sources[start_i:start_i + batch_size]
        targets_batch = targets[start_i:start_i + batch_size]
        # Pad
        pad_sources_batch = np.array(pad_sentence_batch(sources_batch, source_pad_int))
        pad_targets_batch = np.array(pad_sentence_batch(targets_batch, target_pad_int))
        # Need the lengths for the _lengths parameters
        pad_targets_lengths = []
        for target in pad_targets_batch:
            pad_targets_lengths.append(len(target))
        pad_source_lengths = []
        for source in pad_sources_batch:
            pad_source_lengths.append(len(source))
        yield pad_sources_batch, pad_targets_batch, pad_source_lengths, pad_targets_lengths
Explanation: Batch and pad the source and target sequences
End of explanation
def get_accuracy(target, logits):
    
    Calculate accuracy
    
    max_seq = max(target.shape[1], logits.shape[1])
    if max_seq - target.shape[1]:
        target = np.pad(target, [(0, 0), (0, max_seq - target.shape[1])],
                        'constant')
    if max_seq - logits.shape[1]:
        logits = np.pad(logits, [(0, 0), (0, max_seq - logits.shape[1])],
                        'constant')
    return np.mean(np.equal(target, logits))
# Split data to training and validation sets
train_source = source_int_text[batch_size:]
train_target = target_int_text[batch_size:]
valid_source = source_int_text[:batch_size]
valid_target = target_int_text[:batch_size]
(valid_sources_batch, valid_targets_batch, valid_sources_lengths,
 valid_targets_lengths) = next(
     get_batches(valid_source, valid_target, batch_size, source_vocab_to_int[
         '<PAD>'], target_vocab_to_int['<PAD>']))
with tf.Session(graph=train_graph) as sess:
    sess.run(tf.global_variables_initializer())
    for epoch_i in range(epochs):
        for batch_i, (source_batch, target_batch, sources_lengths,
                      targets_lengths) in enumerate(
                          get_batches(train_source, train_target, batch_size,
                                      source_vocab_to_int['<PAD>'],
                                      target_vocab_to_int['<PAD>'])):
            _, loss = sess.run([train_op, cost], {
                input_data: source_batch,
                targets: target_batch,
                lr: learning_rate,
                target_sequence_length: targets_lengths,
                source_sequence_length: sources_lengths,
                keep_prob: keep_probability
            })
            if batch_i % display_step == 0 and batch_i > 0:
                batch_train_logits = sess.run(inference_logits, {
                    input_data:
                    source_batch,
                    source_sequence_length:
                    sources_lengths,
                    target_sequence_length:
                    targets_lengths,
                    keep_prob:
                    1.0
                })
                batch_valid_logits = sess.run(inference_logits, {
                    input_data:
                    valid_sources_batch,
                    source_sequence_length:
                    valid_sources_lengths,
                    target_sequence_length:
                    valid_targets_lengths,
                    keep_prob:
                    1.0
                })
                train_acc = get_accuracy(target_batch, batch_train_logits)
                valid_acc = get_accuracy(valid_targets_batch,
                                         batch_valid_logits)
                print(
                    'Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.4f}, Validation Accuracy: {:>6.4f}, Loss: {:>6.4f}'
                    .format(epoch_i, batch_i,
                            len(source_int_text) // batch_size, train_acc,
                            valid_acc, loss))
    # Save Model
    saver = tf.train.Saver()
    saver.save(sess, save_path)
    print('Model Trained and Saved')
    print('# Number of Epochs         ', epochs)
    print('# Batch Size               ', batch_size)
    print('# RNN Size                 ', rnn_size)
    print('# Number of Layers         ', num_layers)
    print('# Embedding Size           ', encoding_embedding_size)
    print('# Learning Rate            ', learning_rate)
    print('# Dropout Keep Probability ', keep_probability)
Explanation: Train
Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem.
End of explanation
# Save parameters for checkpoint
helper.save_params(save_path)
Explanation: My conclusions from hyperparameter adjustmensts are that for comparable results:
- if you double the batch size, also doouble the number of epochs
- if you half the dropout keep probability, also double the network size
Save Parameters
Save the batch_size and save_path parameters for inference.
End of explanation
import tensorflow as tf
import numpy as np
import helper
import problem_unittests as tests
_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()
load_path = helper.load_params()
Explanation: Checkpoint
End of explanation
def sentence_to_seq(sentence, vocab_to_int):
    
    Convert a sentence to a sequence of ids
    :param sentence: String
    :param vocab_to_int: Dictionary to go from the words to an id
    :return: List of word ids
    
    return [
        vocab_to_int[w] if w in vocab_to_int.keys() else vocab_to_int['<UNK>']
        for w in sentence.lower().split(' ')
    ]
tests.test_sentence_to_seq(sentence_to_seq)
Explanation: Sentence to Sequence
To feed a sentence into the model for translation, you first need to preprocess it.  Implement the function sentence_to_seq() to preprocess new sentences.
Convert the sentence to lowercase
Convert words into ids using vocab_to_int
Convert words not in the vocabulary, to the &lt;UNK&gt; word id.
End of explanation
translate_sentence = 'he saw a old yellow truck .'
translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
    # Load saved model
    loader = tf.train.import_meta_graph(load_path + '.meta')
    loader.restore(sess, load_path)
    input_data = loaded_graph.get_tensor_by_name('input:0')
    logits = loaded_graph.get_tensor_by_name('predictions:0')
    target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_length:0')
    source_sequence_length = loaded_graph.get_tensor_by_name('source_sequence_length:0')
    keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
    translate_logits = sess.run(logits, {input_data: [translate_sentence]*batch_size,
                                         target_sequence_length: [len(translate_sentence)*2]*batch_size,
                                         source_sequence_length: [len(translate_sentence)]*batch_size,
                                         keep_prob: 1.0})[0]
print('Input')
print('  Word Ids:      {}'.format([i for i in translate_sentence]))
print('  English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))
print('\nPrediction')
print('  Word Ids:      {}'.format([i for i in translate_logits]))
print('  French Words: {}'.format(" ".join([target_int_to_vocab[i] for i in translate_logits])))
Explanation: Translate
This will translate translate_sentence from English to French.
End of explanation
import sys
sys.modules.keys()
import types
lines = !conda list
versions = dict()
for line in lines[2:]:
    li = line.split()
    versions[li[0]] = li[1]
def imports():
    print('Modules used in the notebook:\n')
    for val in globals().values():
        if isinstance(val, types.ModuleType):
            name = val.__name__
            ver = ''
            if name in versions:
                ver = versions[name]
            # special case for tensorflow-gpu
            if name + '-gpu' in versions:
                ver = versions[name + '-gpu']
                name = name + '-gpu'
            print('{:25}{:>10}'.format(name, ver))
imports()
Explanation: Imperfect Translation
You might notice that some sentences translate better than others.  Since the dataset you're using only has a vocabulary of 227 English words of the thousands that you use, you're only going to see good results using these words.  For this project, you don't need a perfect translation. However, if you want to create a better translation model, you'll need better data.
You can train on the WMT10 French-English corpus.  This dataset has more vocabulary and richer in topics discussed.  However, this will take you days to train, so make sure you've a GPU and the neural network is performing well on dataset we provided.  Just make sure you play with the WMT10 corpus after you've submitted this project.
Submitting This Project
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_language_translation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.
Appendix
End of explanation
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