def keras_imdb_gru(num_words=None, maxlen=None, embedding_dim=128):
"""
imdb影评二分类，使用gru门控循环单元进行分类
:return:
"""
(x_train, y_train), (x_test, y_test) = imdb.load_data(
os.path.join(root_path, "data", "imdb", "imdb.npz"), num_words=num_words)
if not num_words: num_words = max(max([max(x) for x in x_train]), max([max(x) for x in x_test])) + 1
if not maxlen: maxlen = max(max([len(x) for x in x_train]), max([len(x) for x in x_test])) + 1
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
model = keras.models.Sequential()
model.add(keras.layers.Embedding(num_words + 1, embedding_dim, input_length=maxlen))
model.add(keras.layers.GRU(128))
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.summary()
model.compile(optimizer="rmsprop", loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, epochs=10, batch_size=64, validation_split=0.2, verbose=1)
keras_history_plotcurve(history)
# 评估模型
test_loss, test_accuracy = model.evaluate(x_test, y_test, batch_size=64, verbose=0)
logger.info("\ntest_loss:{0},test_accuracy:{1}".format(test_loss, test_accuracy))
def keras_reuters_gru(num_words=None, maxlen=None, num_categorical=None):
"""
使用gru门控循环单元进行新闻分类，gru包含输入门、输出门、重置门和更新门。
:return:
"""
(X_train, y_train), (X_test, y_test) = reuters.load_data(
path=os.path.join(root_path, "data", "reuters", "reuters.npz"),
num_words=num_words)
if not num_words:
num_words = max(max([max(x)
for x in X_train]), max([max(x)
for x in X_test])) + 1
if not maxlen:
maxlen = max(max([len(x)
for x in X_train]), max([len(x)
for x in X_test])) + 1
if not num_categorical:
num_categorical = max(max(y_train), max(y_test)) + 1
X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
y_train = keras.utils.to_categorical(y_train, num_categorical)
X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
y_test = keras.utils.to_categorical(y_test, num_categorical)
input = keras.layers.Input(shape=(maxlen, ))
x = keras.layers.Embedding(input_dim=num_words, output_dim=128)(input)
x = keras.layers.GRU(32)(x)
x = keras.layers.Dense(128, activation="relu")(x)
x = keras.layers.Dense(num_categorical, activation="softmax")(x)
model = keras.models.Model(inputs=input, outputs=x)
model.summary()
model.compile(optimizer="rmsprop",
loss="categorical_crossentropy",
metrics=["accuracy"])
history = model.fit(X_train,
y_train,
batch_size=64,
epochs=10,
verbose=1,
validation_data=(X_test, y_test))
keras_history_plotcurve(history)
def keras_reuters_cnn():
(X_train, y_train), (X_test, y_test) = reuters.load_data(
path=os.path.join(root_path, "data", "reuters", "reuters.npz"))
num_words = max(max([len(x)
for x in X_train]), max([len(x) for x in X_test])) + 1
num_classify = max(max(y_train), max(y_test)) + 1
num_vocab = max(max([max(x)
for x in X_train]), max([max(x) for x in X_test])) + 1
X_train = sequence.pad_sequences(X_train, maxlen=num_words)
y_train = keras.utils.to_categorical(y_train, num_classify)
X_test = sequence.pad_sequences(X_test, maxlen=num_words)
y_test = keras.utils.to_categorical(y_test, num_classify)
input = keras.layers.Input(shape=(num_words, ))
x = keras.layers.Embedding(input_dim=num_vocab + 1, output_dim=128)(input)
x = keras.layers.Convolution1D(32, 5, activation="relu")(x)
x = keras.layers.Convolution1D(32, 5, activation="relu")(x)
x = keras.layers.MaxPooling1D(5)(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Convolution1D(32, 5, activation="relu")(x)
x = keras.layers.Convolution1D(32, 5, activation="relu")(x)
x = keras.layers.MaxPooling1D(5)(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Flatten()(x)
x = keras.layers.Dense(128, activation="relu")(x)
x = keras.layers.Dense(num_classify, activation="softmax")(x)
model = keras.models.Model(inputs=input, outputs=x)
model.summary()
model.compile(optimizer="rmsprop",
loss="categorical_crossentropy",
metrics=["accuracy"])
history = model.fit(X_train,
y_train,
batch_size=64,
epochs=10,
verbose=1,
validation_data=(X_test, y_test))
keras_history_plotcurve(history)
def keras_imdb_mlp(num_words=None, maxlen=None):
"""
imdb分类，构建多层感知机进行分类
:param num_words:
:param maxlen:
:return:
"""
(x_train, y_train), (x_test, y_test) = imdb.load_data(
os.path.join(root_path, "data", "imdb", "imdb.npz"), num_words=num_words)
if not num_words: num_words = max(max([max(x) for x in x_train]), max([max(x) for x in x_test])) + 1
if not maxlen: maxlen = max(max([len(x) for x in x_train]), max([len(x) for x in x_test])) + 1
tokenizer = text.Tokenizer(num_words=num_words)
x_train = tokenizer.sequences_to_matrix(x_train, mode='binary')
x_test = tokenizer.sequences_to_matrix(x_test, mode='binary')
input = keras.layers.Input(shape=(num_words,))
x = keras.layers.Dense(512, activation="relu", kernel_initializer="glorot_normal")(input)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Dropout(0.25)(x)
x = keras.layers.Dense(256, activation="relu")(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Dropout(0.25)(x)
x = keras.layers.Dense(128, activation="relu")(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Dropout(0.25)(x)
x = keras.layers.Dense(1, activation="sigmoid")(x)
model = keras.models.Model(inputs=input, outputs=x)
model.summary()
model.compile(optimizer="adadelta", loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, epochs=10, batch_size=64, validation_split=0.2, verbose=1)
keras_history_plotcurve(history)
# 评估模型
test_loss, test_accuracy = model.evaluate(x_test, y_test, batch_size=64, verbose=0)
logger.info("\ntest_loss:{0},test_accuracy:{1}".format(test_loss, test_accuracy))
def keras_imdb_fasttext(num_words=None, maxlen=None, batch_size=32, embedding_dims=50, epochs=5, ngram_range=1):
"""
ngram 训练
:param num_words: 词典单词数量
:param maxlen: 句子长度
:param batch_size: 批量大下
:param embedding_dims: 嵌入层输出层数
:param epochs: 训练轮数
:param ngram_range: ngram数量
:return:
"""
def create_ngram_set(input_list, ngram_value=2):
"""
Extract a set of n-grams from a list of integers.
>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2)
{(4, 9), (4, 1), (1, 4), (9, 4)}
>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3)
[(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)]
"""
return set(zip(*[input_list[i:] for i in range(ngram_value)]))
def add_ngram(sequences, token_indice, ngram_range=2):
"""
Augment the input list of list (sequences) by appending n-grams values.
Example: adding bi-gram
>>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
>>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017}
>>> add_ngram(sequences, token_indice, ngram_range=2)
[[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42]]
Example: adding tri-gram
>>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
>>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017, (7, 9, 2): 2018}
>>> add_ngram(sequences, token_indice, ngram_range=3)
[[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42, 2018]]
"""
new_sequences = []
for input_list in sequences:
new_list = input_list[:]
for ngram_value in range(2, ngram_range + 1):
for i in range(len(new_list) - ngram_value + 1):
ngram = tuple(new_list[i:i + ngram_value])
if ngram in token_indice:
new_list.append(token_indice[ngram])
new_sequences.append(new_list)
return new_sequences
print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(
os.path.join(root_path, "data", "imdb", "imdb.npz"), num_words=num_words)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))
print('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))
if not num_words: num_words = max(max([max(x) for x in x_train]), max([max(x) for x in x_test])) + 1
if not maxlen: maxlen = max(max([len(x) for x in x_train]), max([len(x) for x in x_test])) + 1
if ngram_range > 1:
print('Adding {}-gram features'.format(ngram_range))
ngram_set = set()
for input_list in x_train:
for i in range(2, ngram_range + 1):
set_of_ngram = create_ngram_set(input_list, ngram_value=i)
ngram_set.update(set_of_ngram)
# Dictionary mapping n-gram token to a unique integer.
# Integer values are greater than max_features in order
# to avoid collision with existing features.
start_index = num_words + 1
token_indice = {v: k + start_index for k, v in enumerate(ngram_set)}
indice_token = {token_indice[k]: k for k in token_indice}
# max_features is the highest integer that could be found in the dataset.
num_words = np.max(list(indice_token.keys())) + 1
# Augmenting x_train and x_test with n-grams features
x_train = add_ngram(x_train, token_indice, ngram_range)
x_test = add_ngram(x_test, token_indice, ngram_range)
print('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))
print('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))
print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
print('Build model...')
model = keras.models.Sequential()
# we start off with an efficient embedding layer which maps
# our vocab indices into embedding_dims dimensions
model.add(keras.layers.Embedding(num_words, embedding_dims, input_length=maxlen))
# we add a GlobalAveragePooling1D, which will average the embeddings
# of all words in the document
model.add(keras.layers.GlobalAveragePooling1D())
# We project onto a single unit output layer, and squash it with a sigmoid:
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.summary()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test))
keras_history_plotcurve(history)
def keras_reuters_mlp(num_words=None,
maxlen=None,
num_categorical=None,
batch_size=32,
epochs=10,
mode=None):
"""
使用mlp多层感知机模式进行情感评估,同时对比自归一化mlp和常规mlp的性能对比
:return:
"""
(X_train, y_train), (X_test, y_test) = reuters.load_data(
path=os.path.join(root_path, "data", "reuters", "reuters.npz"),
num_words=num_words)
if not num_words:
num_words = max(max([max(x)
for x in X_train]), max([max(x)
for x in X_test])) + 1
if not maxlen:
maxlen = max(max([len(x)
for x in X_train]), max([len(x)
for x in X_test])) + 1
if not num_categorical:
num_categorical = max(max(y_train), max(y_test)) + 1
tokenizer = text.Tokenizer(num_words=num_words)
X_train = tokenizer.sequences_to_matrix(X_train)
y_train = keras.utils.to_categorical(y_train, num_categorical)
X_test = tokenizer.sequences_to_matrix(X_test)
y_test = keras.utils.to_categorical(y_test, num_categorical)
input = keras.layers.Input(shape=(num_words, ))
# 自归一化snn
if mode == "self-normalizing":
x = keras.layers.Dense(512,
activation=keras.activations.selu,
kernel_initializer="lecun_normal")(input)
x = keras.layers.AlphaDropout(0.1)(x)
x = keras.layers.Dense(256,
activation="selu",
kernel_initializer="lecun_normal")(x)
x = keras.layers.AlphaDropout(0.1)(x)
x = keras.layers.Dense(128,
activation="selu",
kernel_initializer="lecun_normal")(x)
x = keras.layers.AlphaDropout(0.1)(x)
else:
x = keras.layers.Dense(512,
activation="relu",
kernel_initializer="glorot_normal")(input)
x = keras.layers.BatchNormalization()(x)
# x = keras.layers.Dropout(0.4)(x)
x = keras.layers.Dense(256,
activation="relu",
kernel_initializer="glorot_normal")(x)
x = keras.layers.BatchNormalization()(x)
# x = keras.layers.Dropout(0.4)(x)
x = keras.layers.Dense(128,
activation="relu",
kernel_initializer="glorot_normal")(x)
x = keras.layers.BatchNormalization()(x)
# x = keras.layers.Dropout(0.4)(x)
x = keras.layers.Dense(num_categorical, activation="softmax")(x)
model = keras.models.Model(inputs=input, outputs=x)
model.summary()
model.compile(optimizer="adadelta",
loss="categorical_crossentropy",
metrics=["accuracy"])
history = model.fit(X_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2)
keras_history_plotcurve(history)
score = model.evaluate(X_test, y_test, batch_size=batch_size, verbose=1)
logger.info('Test loss:{0}'.format(score[0]))
logger.info('Test accuracy:{0}'.format(score[1]))