Demonstrate loading and running a saved seq2seq model. (keras-team#9119)

* Demonstrate loading and running a saved seq2seq model.

* formatting fixes

* formatting fixes

examples/lstm_seq2seq.py

@@ -66,7 +66,7 @@
 target_texts = []
 input_characters = set()
 target_characters = set()
-lines = open(data_path).read().split('\n')
+lines = open(data_path, 'r', encoding='utf-8').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
@@ -220,7 +220,7 @@ def decode_sequence(input_seq):
 
 
 for seq_index in range(100):
-    # Take one sequence (part of the training test)
+    # Take one sequence (part of the training set)
     # for trying out decoding.
     input_seq = encoder_input_data[seq_index: seq_index + 1]
     decoded_sentence = decode_sequence(input_seq)

examples/lstm_seq2seq_restore.py

@@ -0,0 +1,149 @@
+'''Restore a character-level sequence to sequence model from disk and use it
+to generate predictions.
+
+This script loads the s2s.h5 model saved by lstm_seq2seq.py and generates
+sequences from it.  It assumes that no changes have been made (for example:
+latent_dim is unchanged, and the input data and model architecture are unchanged).
+
+See lstm_seq2seq.py for more details on the model architecture and how
+it is trained.
+'''
+from __future__ import print_function
+
+from keras.models import Model, load_model
+from keras.layers import Input
+import numpy as np
+
+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.  We use the same approach as the training script.
+# NOTE: the data must be identical, in order for the character -> integer
+# mappings to be consistent.
+# We omit encoding target_texts since they are not needed.
+input_texts = []
+target_texts = []
+input_characters = set()
+target_characters = set()
+lines = open(data_path, 'r', encoding='utf-8').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)
+num_decoder_tokens = len(target_characters)
+max_encoder_seq_length = max([len(txt) for txt in input_texts])
+max_decoder_seq_length = max([len(txt) for txt in target_texts])
+
+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')
+
+for i, input_text in enumerate(input_texts):
+    for t, char in enumerate(input_text):
+        encoder_input_data[i, t, input_token_index[char]] = 1.
+
+# Restore the model and construct the encoder and decoder.
+model = load_model('s2s.h5')
+
+encoder_inputs = model.input[0]   # input_1
+encoder_outputs, state_h_enc, state_c_enc = model.layers[2].output   # lstm_1
+encoder_states = [state_h_enc, state_c_enc]
+encoder_model = Model(encoder_inputs, encoder_states)
+
+decoder_inputs = model.input[1]   # input_2
+decoder_state_input_h = Input(shape=(latent_dim,), name='input_3')
+decoder_state_input_c = Input(shape=(latent_dim,), name='input_4')
+decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
+decoder_lstm = model.layers[3]
+decoder_outputs, state_h_dec, state_c_dec = decoder_lstm(
+    decoder_inputs, initial_state=decoder_states_inputs)
+decoder_states = [state_h_dec, state_c_dec]
+decoder_dense = model.layers[4]
+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())
+
+
+# Decodes an input sequence.  Future work should support beam search.
+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 set)
+    # 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)