在大输入上滑动张量流分类器

Question

我有一个分类器（在 tensorflow 2.2 中实现），输入的小浮点向量形状为 (None,500,1) 和一个热输出 (None,100)。我想在将它“滑动”到一个更大的向量时训练相同的架构：输入应该是 (None,5000) 并且输出将是它在大向量上的结果的结果：(None ,10,100) 将与平均值等函数聚合以获得一个热门结果 (None,100)。我希望在模型中实现这个 而无需 将数据向量分成几部分。

#小输入简化工作模型：

from tensorflow.keras.layers import Input,Conv1D,LSTM,Dense,Flatten
from tensorflow.keras import Model
import tensorflow as tf
import numpy as np

input_vector_length=500
output_size=100
filters, units,kernel_size=4,4,4

small_inp = Input((input_vector_length, 1))
x = Conv1D(filters, kernel_size=kernel_size, activation='relu')(small_inp)
x = LSTM(units, return_sequences=True)(x)
x = Flatten()(x)
preds= Dense(output_size, activation='softmax')(x)
model = Model(inputs=small_inp, outputs=preds)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

#生成数据和训练：

X=np.random.normal(size=(200,500,1))
labels=np.random.randint(0,100,(200,))
y=np.eye(100)[labels]
model.fit(X,y,epochs=5)

Answer 1

可以使用Lambda结合Concatenate，思路是用Lambda分割输入张量，最后用Concatenate结合输出。这是一个工作示例

from tensorflow.keras.layers import Input,Conv1D,LSTM,Dense,Flatten
from tensorflow.keras import Model
import tensorflow as tf
import numpy as np

# the additional modules you need to import
from tensorflow.keras.layers import Lambda,Concatenate
from tensorflow.keras import Sequential


filters,units,kernel_size = 4,4,4

# create a smaller example dataset
input_vector_length = 10
output_size = 4
num_samples = 5

X = np.ones((1,input_vector_length*num_samples,1))
labels = np.random.randint(0,output_size,(num_samples,))
y = np.eye(output_size)[labels]
y = np.concatenate(y,axis=0)[None,:]

# get the slices of the input layer
start_inds = range(input_vector_length*num_samples+input_vector_length)[::input_vector_length]
start_pairs = [[start_inds[ind],start_inds[ind+1]] for ind in range(len(start_inds)-1)]

conv_layer = Conv1D(filters,kernel_size=kernel_size,activation='relu')
lstm_layer = LSTM(units,return_sequences=True)
flatten_layer = Flatten()
dense_layer = Dense(output_size,activation='softmax')
block_op = Sequential([conv_layer,lstm_layer,flatten_layer,dense_layer])
# create slices of the input layer, return a list of input Tensors
slice_input = Lambda(lambda x: [x[:,inds[0]:inds[1]] for inds in start_pairs])
concat_layer = Concatenate(axis=1)

small_inp = Input(shape=(input_vector_length*num_samples,1),batch_size=1)
preds = concat_layer([block_op(x_slice) for x_slice in slice_input(small_inp)])
model = Model(inputs=small_inp,outputs=preds)
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
model.fit(X,y,epochs=5)

pred = model.predict(X)

Answer 2

您可以为此使用 tf.data.Dataset.window。

import tensorflow as tf

x = tf.reshape(tf.range(1, 1000 + 1), (-1, 1))
y = tf.random.uniform((1000, 1), minval=0, maxval=2, dtype=tf.int32)

ds = tf.data.Dataset.from_tensor_slices((x, y))
ds = ds.window(size=10, shift=1, stride=1)
ds = ds.flat_map(lambda x, y: tf.data.Dataset.zip((x, y)).take(4).batch(4))

for batch in ds.take(10):
    print(batch[0])

tf.Tensor(
[[1], [2], [3], [4]], shape=(4, 1), dtype=int32)
tf.Tensor(
[[2], [3], [4], [5]], shape=(4, 1), dtype=int32)
tf.Tensor(
[[3], [4], [5], [6]], shape=(4, 1), dtype=int32)
tf.Tensor(
[[4], [5], [6], [7]], shape=(4, 1), dtype=int32)
tf.Tensor(
[[5], [6], [7], [8]], shape=(4, 1), dtype=int32)
tf.Tensor(
[[6], [7], [8], [9]], shape=(4, 1), dtype=int32)
tf.Tensor(
[[7], [8], [9], [10]], shape=(4, 1), dtype=int32)

如您所见，批次是 “滑动”，因为它们是重叠的。

在大输入上滑动张量流分类器

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