Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud.
あなたは、あなたのJupyter notebookをアップロードして、クラウドで走らせることが出来ます。
熱波をPythonで見る
あなたのApple Health DataをPythonで探索する
JupyterLabか、PyCharmか
JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data.
私自身は、PyCharmの方が好きですが、状況によっては JupyterLab がベストということもあるでしょう。
いろんなタイプのdockerイメージがあります。
$ docker run --rm -p 10000:8888 -e JUPYTER_ENABLE_LAB=yes -v "$PWD":/home/jovyan/work jupyter/scipy-notebook:17aba6048f44
QLF? (2)
この方が、それらしいですか？
時間軸上でシフトを加えると、うまくいきません。
import matplotlib.pyplot as plt
import numpy as np
from sklearn.manifold import TSNE
alphabet = list("abcdefghijklmnopqrstuvwxyz")
values = ['111000111111111000', '111111111000111000111000111000', '111111111000111000111111111000111000', '111111111000111000111000', '111000',
'111000111000111111111000111000', '111111111000111111111000111000', '111000111000111000111000', '111000111000', '111000111111111000111111111000111111111000',
'111111111000111000111111111000', '111000111111111000111000111000', '111111111000111111111000', '111111111000111000', '111111111000111111111000111111111000',
'111000111111111000111111111000111000', '111111111000111111111000111000111111111000', '111000111111111000111000', '111000111000111000', '111111111000',
'111000111000111111111000', '111000111000111000111111111000', '111000111111111000111111111000', '111111111000111000111000111111111000', '111111111000111000111111111000111111111000',
'111111111000111111111000111000111000']
morse_dict = dict(zip(alphabet, values))
nrepeat = 100
n = len(values)
word_len = 50
code_len_max = 0
for v in values:
code_len_max = max(code_len_max, len(v))
print("code_len_max = ", code_len_max)
X = np.zeros((n * nrepeat, word_len))
Y = np.zeros(n * nrepeat, dtype=np.int)
for rep in range(nrepeat):
for i, letter in enumerate(alphabet):
joffset = int(np.random.uniform(1, word_len - code_len_max))
for j in range(word_len):
X[i + rep * n][j] = np.random.normal(0.0, 0.2)
for j, char in enumerate(morse_dict[letter]):
X[i+rep * n][j+joffset] = X[i+rep * n][j+joffset] + (ord(char) - ord('0'))
Y[i+rep * n] = i
X_reduced = TSNE(n_components=2, random_state=0, perplexity=50).fit_transform(X)
plt.figure(figsize=(8, 12))
plt.subplot(3, 1, 1)
x = np.arange(word_len)
for i in range(3):
y = X[i, :] + 2.0 * i
plt.plot(x, y)
plt.grid()
plt.title('Waveform')
plt.subplot(3, 1, 2)
plt.scatter(X_reduced[:, 0], X_reduced[:, 1],
c=Y, edgecolors='black', alpha=0.5)
plt.colorbar()
plt.title('t-SNE')
plt.subplot(3, 1, 3)
for rep in range(min(3, nrepeat)):
for i, letter in enumerate(alphabet):
s = chr(Y[i] + ord('a'))
plt.text(X_reduced[i+rep*n, 0], X_reduced[i+rep*n, 1], s)
plt.xlim([min(X_reduced[:, 0]), max(X_reduced[:, 0])])
plt.ylim([min(X_reduced[:, 1]), max(X_reduced[:, 1])])
plt.title('t-SNE')
plt.show()
QLF?
t-distributed Stochastic Neighbor Embedding (t-SNE)は、高次元のデータを可視化するためのツールです。
モールスコードでａからｚを表している波形を、t-SNEを用いて２次元で可視化しています。
各信号は電気的に生成された後、ガウス雑音が加えられています。
import matplotlib.pyplot as plt
import numpy as np
from sklearn.manifold import TSNE
alphabet = list("abcdefghijklmnopqrstuvwxyz")
values = ['101110', '1110101010', '111010111010', '11101010', '10',
'1010111010', '1110111010', '10101010', '1010', '10111011101110',
'1110101110', '1011101010', '11101110', '111010', '111011101110',
'101110111010', '11101110101110', '10111010', '101010', '1110',
'10101110', '1010101110', '1011101110', '111010101110', '11101011101110',
'111011101010']
morse_dict = dict(zip(alphabet, values))
nrepeat = 100
n = len(values)
word_len = 15
X = np.zeros((n * nrepeat, word_len))
Y = np.zeros(n * nrepeat, dtype=np.int)
for rep in range(nrepeat):
for i, letter in enumerate(alphabet):
for j, char in enumerate(morse_dict[letter]):
X[i+rep * n][j+1] = (ord(char) - ord('0')) + np.random.normal(0.0, 0.2)
Y[i+rep * n] = i
X_reduced = TSNE(n_components=2, random_state=0).fit_transform(X)
plt.figure(figsize=(8, 12))
plt.subplot(3, 1, 1)
x = np.arange(word_len)
for i in range(3):
y = X[i, :] + 2.0 * i
plt.plot(x, y)
plt.grid()
plt.title('Waveform')
plt.subplot(3, 1, 2)
plt.scatter(X_reduced[:, 0], X_reduced[:, 1],
c=Y, edgecolors='black', alpha=0.5)
plt.colorbar()
plt.title('t-SNE')
plt.subplot(3, 1, 3)
for rep in range(min(3, nrepeat)):
for i, letter in enumerate(alphabet):
s = chr(Y[i] + ord('a'))
plt.text(X_reduced[i+rep*n, 0], X_reduced[i+rep*n, 1], s)
plt.xlim([min(X_reduced[:, 0]), max(X_reduced[:, 0])])
plt.ylim([min(X_reduced[:, 1]), max(X_reduced[:, 1])])
plt.title('t-SNE')
plt.show()
あなたはCQとかQRZは嫌いなのですね
- Input sentence: -.-. --.- Decoded sentence: cy - Input sentence: --.- .-. -- Decoded sentence: qrm - Input sentence: --.- .-. --.. Decoded sentence: zrz - Input sentence: --.- ... -... Decoded sentence: qub - Input sentence: -.-- .- -... -... .-.. . Decoded sentence: yabble - Input sentence: -... .-. .- -. -.. Decoded sentence: brand - Input sentence: .-. . -.. --- .-- .- Decoded sentence: redowa - Input sentence: -.-. .- -- .. --- -. Decoded sentence: camion - Input sentence: .-. . -. -.. Decoded sentence: rend - Input sentence: -... .- .- .-. Decoded sentence: baar
上の単語は、全てトレーニング系列には含まれていないことに注意して下さい。
明らかに、私たちは私たちの辞書にプロサインとかQ符号を付け加えて拡張することが必要なようです。
from keras.models import Model
from keras.layers import Input, LSTM, Dense
import numpy as np
import random
import matplotlib.pyplot as plt
batch_size = 64
epochs = 100
latent_dim = 256
num_samples = 20000
data_path = '../keras015/words_morse.txt'
max_word_length = 6
lines = []
input_texts = []
target_texts = []
input_characters = set()
target_characters = set()
with open(data_path, 'r', encoding='utf-8') as f:
for line in f:
english_text, morse_text = line.split(', ')
if len(english_text) <= max_word_length:
lines.append(line.rstrip('\n'))
print("max_word_length = ", max_word_length)
print("no. of available words =", len(lines))
num_samples = min(num_samples, len(lines))
print("no. of words sampled = ", num_samples)
lines_sampled = random.sample(lines, k=num_samples)
lines_sampled[0] = 'cq, -.-. --.-'
lines_sampled[1] = 'qrm, --.- .-. --'
lines_sampled[2] = 'qrz, --.- .-. --..'
lines_sampled[3] = 'qsb, --.- ... -...'
print(lines_sampled[:10])
for line in lines_sampled:
target_text, input_text = line.split(', ')
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')
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.
m = len(encoder_input_data) // 4
(input_texts_val, input_texts_train) =\
input_texts[:m], input_texts[m:]
(encoder_input_data_val, encoder_input_data_train) =\
encoder_input_data[:m], encoder_input_data[m:]
(decoder_input_data_val, decoder_input_data_train) =\
decoder_input_data[:m], decoder_input_data[m:]
(decoder_target_data_val, decoder_target_data_train) =\
decoder_target_data[:m], decoder_target_data[m:]
print(len(encoder_input_data_val), len(encoder_input_data_train))
# 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)
# Define the model that will turn
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
# Run training
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model.summary()
hist = model.fit([encoder_input_data_train, decoder_input_data_train], decoder_target_data_train,
validation_data=([encoder_input_data_val, decoder_input_data_val], decoder_target_data_val),
batch_size=batch_size, epochs=epochs,
verbose=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
def main():
for seq_index in range(10):
# Take one sequence (part of the training set)
# for trying out decoding.
input_seq = encoder_input_data_val[seq_index: seq_index + 1]
decoded_sentence = decode_sequence(input_seq)
print('-')
print('Input sentence:', input_texts_val[seq_index])
print('Decoded sentence:', decoded_sentence)
print(hist.history.keys())
plt.figure(figsize=(16, 5))
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.show()
main()
CWが話せるなら、CWが読める
a, .-
aa, .- .-
aal, .- .- .-..
aalii, .- .- .-.. .. ..
(many lines deleted)
zythia, --.. -.-- - .... .. .-
zythum, --.. -.-- - .... ..- --
zyzomys, --.. -.-- --.. --- -- -.-- ...
zyzzogeton, --.. -.-- --.. --.. --- --. . - --- -.
同じトレーニング系列を用いて、入力とターゲットとを逆にしてみます。
# input_text, target_text = line.split(', ')
target_text, input_text = line.split(', ')
トレーニングをしばらく行うと、さて、CWが読めるようになりました！
- Input sentence: .- -- . -. -.. . Decoded sentence: amende - Input sentence: ... - --- -.-. .- .... Decoded sentence: stocah - Input sentence: --. .-. --- .--. . Decoded sentence: grope - Input sentence: -... --- --. .- -. Decoded sentence: bogan - Input sentence: .. -- -... . .-. Decoded sentence: imber - Input sentence: -... .- -.-. -.-. .- Decoded sentence: bacca - Input sentence: .. -. -.. ..- -.-. . Decoded sentence: induce - Input sentence: ..-. .- -. Decoded sentence: fan - Input sentence: -.. .. .-. -.. Decoded sentence: dird - Input sentence: .- .-.. .-.. .. . Decoded sentence: allie
悪く無いでしょう？
max_word_lenght = 6
no. of available words = 33887
no. of words sampled = 10000
['amende, .- -- . -. -.. .\n', 'stocah, ... - --- -.-. .- ....\n', 'grope, --. .-. --- .--. .\n']
Number of samples: 10000
Number of unique input tokens: 4
Number of unique output tokens: 28
Max sequence length for inputs: 29
Max sequence length for outputs: 8
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, None, 4) 0
__________________________________________________________________________________________________
input_2 (InputLayer) (None, None, 28) 0
__________________________________________________________________________________________________
lstm_1 (LSTM) [(None, 256), (None, 267264 input_1[0][0]
__________________________________________________________________________________________________
lstm_2 (LSTM) [(None, None, 256), 291840 input_2[0][0]
lstm_1[0][1]
lstm_1[0][2]
__________________________________________________________________________________________________
dense_1 (Dense) (None, None, 28) 7196 lstm_2[0][0]
==================================================================================================
Total params: 566,300
Trainable params: 566,300
Non-trainable params: 0
__________________________________________________________________________________________________
Train on 8000 samples, validate on 2000 samples
Epoch 1/50
from keras.models import Model
from keras.layers import Input, LSTM, Dense
import numpy as np
import random
import matplotlib.pyplot as plt
batch_size = 64 # Batch size for training.
epochs = 50 # Number of epochs to train for.
latent_dim = 256 # Latent dimensionality of the encoding space.
num_samples = 10000 # Number of samples to train on.
data_path = '../keras015/words_morse.txt'
max_word_length = 6
# Vectorize the data.
lines = []
input_texts = []
target_texts = []
input_characters = set()
target_characters = set()
with open(data_path, 'r', encoding='utf-8') as f:
for line in f:
english_text, morse_text = line.split(', ')
if len(english_text) <= max_word_length:
lines.append(line)
print("max_word_lenght = ", max_word_length)
print("no. of available words =", len(lines))
num_samples = min(num_samples, len(lines))
print("no. of words sampled = ", num_samples)
lines_sampled = random.sample(lines, k=num_samples)
print(lines_sampled[:3])
for line in lines_sampled:
# input_text, target_text = line.split(', ')
target_text, input_text = line.split(', ')
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')
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.
# 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)
# Define the model that will turn
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
# Run training
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model.summary()
hist = 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
def main():
for seq_index in range(10):
# 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)
print(hist.history.keys())
plt.figure(figsize=(16, 5))
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.show()
main()
仏語が話せるなら、CWも話せる
これは、Kerasのサンプルコードです。英語の文を仏語に翻訳します。
英語と仏語の文のペアを用いて、モデルをトレーニングします。
Is she Japanese? Est-elle japonaise ? Is she a doctor? Est-elle médecin ?
私のMac miniで１時間ほどすると、このような結果が得られます。
Input sentence: Be nice. Decoded sentence: Soyez gentil ! - Input sentence: Drop it! Decoded sentence: Laissez tomber ! - Input sentence: Get out! Decoded sentence: Sortez !
ここまでは、良いですね。しかしながら、私たちが本当に知りたいのは、以下のようなトレーニング系列を与えた時に何が起きるかです。
a, .-
aa, .- .-
aal, .- .- .-..
aalii, .- .- .-.. .. ..
(many lines deleted)
antidivorce, .- -. - .. -.. .. ...- --- .-. -.-. .
antidogmatic, .- -. - .. -.. --- --. -- .- - .. -.-.
antidomestic, .- -. - .. -.. --- -- . ... - .. -.-.
antidominican, .- -. - .. -.. --- -- .. -. .. -.-. .- -.
しばらく時間が経過したのち（トレーニング系列のサイズによりますが）、このようになります。
Number of samples: 10000 Number of unique input tokens: 26 Number of unique output tokens: 5 Max sequence length for inputs: 23 Max sequence length for outputs: 95 Train on 8000 samples, validate on 2000 samples Epoch 1/100 - Input sentence: abbacy Decoded sentence: .- -... -... .- -.-. -.-- - Input sentence: abbadide Decoded sentence: .- -... -... .- -.. .. -.. . - Input sentence: abbas Decoded sentence: .- -... -... .- ... Process finished with exit code 0
この特定の例では、トレーニング系列に含まれるサンプルをデコードしていることに留意してください。
from keras.models import Model
from keras.layers import Input, LSTM, Dense
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.
# num_samples = 5
data_path = 'seq2seq.txt'
data_path = 'words_morse.txt'
# Vectorize the data.
input_texts = []
target_texts = []
input_characters = set()
target_characters = set()
with open(data_path, 'r', encoding='utf-8') as f:
lines = f.read().split('\n')
for line in lines[: min(num_samples, len(lines) - 1)]:
# input_text, target_text = line.split('\t')
input_text, target_text = line.split(', ')
print("input_text [", input_text, "]", sep="")
print("target_text [", target_text, "]", sep="")
# 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')
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.
# 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)
# Define the model that will turn
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
# Run training
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
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
def main():
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)
main()
CWデコードとディープラーニング（４）
また別のタイプのトレーニングシーケンスは、こんな感じです。
a, 10111000
a, 10111000
aa, 1011100010111000
(many lines deleted)
zythia, 111011101010001110101110111000111000101010100010100010111000
zythum, 111011101010001110101110111000111000101010100010101110001110111000
zyzomys, 11101110101000111010111011100011101110101000111011101110001110111000111010111011100010101000
zyzzogeton, 11101110101000111010111011100011101110101000111011101010001110111011100011101110100010001110001110111011100011101000
“000”は、文字間のスペースを表しています。
11101011101000111011101110001110101110100010111000 coca coca 1110101110100010111000101010001000 case case 111010111010001011100010111010001110101000 card card 101010001011101110001010001110111000 swim swim 11101110100010111000101011101000101011101000 gaff gaff 11101011101000101110001010111010001010101000 cafh caln 1010101000101000111010001110101000 hind hind
さらに、別のタイプです。
a, 1 111
aa, 1 111 1 111
aal, 1 111 1 111 1 111 1 1
aalii, 1 111 1 111 1 111 1 1 1 1 1 1
(many lines deleted)
zythia, 111 111 1 1 111 1 111 111 111 1 1 1 1 1 1 1 111
zythum, 111 111 1 1 111 1 111 111 111 1 1 1 1 1 1 111 111 111
zyzomys, 111 111 1 1 111 1 111 111 111 111 1 1 111 111 111 111 111 111 1 111 111 1 1 1
zyzzogeton, 111 111 1 1 111 1 111 111 111 111 1 1 111 111 1 1 111 111 111 111 111 1 1 111 111 111 111 111 1
この方が、あなたには読みやすいですか。
1 1 111 1 1 1 111 1 1 111 111 111 fido fido 1 111 111 1 1 1 111 1 1 adai adai 1 111 1 1 111 111 1 1 1 111 rada rada 1 111 1 111 1 1 1 111 111 alem alem 1 111 111 1 1 1 111 1 111 1 1 pice pice 1 111 111 1 111 111 1 111 1 111 111 eggy egcy 1 111 111 1 1 111 1 111 1 1 1 pale pale
以下の例では、４文字未満の単語も含まれています。
1 111 1 1 ai ai 111 1 111 111 111 111 111 1 1 111 you you 111 1 1 111 111 1 1 111 tutu tutu 1 1 111 1 111 111 111 111 1 1 111 111 1 111 111 foxy foxy 1 1 1 1 111 111 111 111 1 1 hoti hott 111 1 111 1 1 1 111 111 1 1 111 cepa cepa 111 111 1 1 1 111 111 gut gut
from keras.models import Sequential
from keras import layers
import numpy as np
import matplotlib.pyplot as plt
class CharTable(object):
def __init__(self, chars):
self.chars = sorted(set(chars))
self.char_indices = dict((c, i) for i, c in enumerate(self.chars))
self.indices_char = dict((i, c) for i, c in enumerate(self.chars))
def encode(self, token, num_rows):
x = np.zeros((num_rows, len(self.chars)))
for i, c in enumerate(token):
x[i, self.char_indices] = 1
return x
def decode(self, x, calc_argmax=True):
if calc_argmax:
x = [x.argmax(axis=-1)]
return ''.join(self.indices_char[int(v)] for v in x)
def main():
word_len = 4
max_len_x = 15 * word_len + 2*(word_len - 1)
max_len_y = word_len
input_list = []
output_list = []
fin = 'words_morse1only.txt'
with open(fin, 'r') as file:
for line in file.read().splitlines():
mylist = line.split(", ")
[word, morse] = mylist
morse = morse + ' ' * (max_len_x - len(morse))
if len(word) <= word_len:
word = word + ' ' * (word_len - len(word))
input_list.append(morse)
output_list.append(word)
print("input_list = ", input_list[:5])
print("output_list = ", output_list[:5])
# chars_in = '10 '
chars_in = '1 '
chars_out = 'abcdefghijklmnopqrstuvwxyz '
ctable_in = CharTable(chars_in)
ctable_out = CharTable(chars_out)
x = np.zeros((len(input_list), max_len_x, len(chars_in)))
y = np.zeros((len(output_list), max_len_y, len(chars_out)))
for i, token in enumerate(input_list):
x[i] = ctable_in.encode(token, max_len_x)
for i, token in enumerate(output_list):
y[i] = ctable_out.encode(token, max_len_y)
indices = np.arange(len(y))
np.random.shuffle(indices)
x = x[indices]
y = y[indices]
m = len(x) - 100
(x_train, x_val) = x[:m], x[m:]
(y_train, y_val) = y[:m], y[m:]
hidden_size = 64
batch_size = 128
nlayers = 1
epochs = 600
model = Sequential()
model.add(layers.LSTM(hidden_size, input_shape=(max_len_x, len(chars_in))))
model.add(layers.RepeatVector(word_len))
for _ in range(nlayers):
model.add(layers.LSTM(hidden_size, return_sequences=True))
model.add(layers.TimeDistributed(layers.Dense(len(chars_out), activation='softmax')))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
hist = model.fit(x_train, y_train, batch_size=batch_size,
epochs=epochs, verbose=2, validation_data=(x_val, y_val))
predict = model.predict_classes(x_val)
for i in range(len(x_val)):
print("".join([ctable_in.decode(code) for code in x_val[i]]),
"".join([ctable_out.decode(code) for code in y_val[i]]), end=" ")
for j in range(word_len):
print(ctable_out.indices_char[predict[i][j]], end="")
print()
plt.figure(figsize=(16, 5))
plt.subplot(121)
plt.plot(hist.history['acc'])
plt.plot(hist.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.subplot(122)
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.show()
main()