python - 如何在 Tensorflow 和 Keras 中正确建模 LSTM

问题描述

我有一个 CSV 格式的数据集，如下所示：

 1,dont like the natives
 2,Keep it local always
 2,Karibu kenya

标签1表示仇恨言论，而2表示正面。

这是我的代码：

import numpy as np
import csv

import tensorflow as tf
from tensorflow.keras.layers import (
            Masking, LSTM, Dense, TimeDistributed, Activation)

def tokenize(text):
    """
    Change text string into number and
    make sure they resulting np.array is of the same size
    """
    Tokenizer = tf.keras.preprocessing.text.Tokenizer

    t = Tokenizer()
    t.fit_on_texts(text)
    tokenized_text = t.texts_to_sequences(text)

    tokenized_text = [item for sublist in tokenized_text for item in sublist]
    return np.resize(np.array(tokenized_text), (1, 30))


x_train = []
y_train = []

# Reading data from CSV
with open('data.csv') as csv_file:
    csv_reader = csv.reader(csv_file, delimiter=',')
    line_count = 0
    for row in csv_reader:
      line_count = line_count+1
      if line_count == 1:
        continue

      # Tokenize input data
      tokenized = tokenize(row[1])
      x_train.append(tokenized)
      y_train.append(row[0])

x_train = np.array(x_train).astype('float32')
y_train = np.array(y_train).astype('float32')

x_test = x_train[:3]
y_test = y_train[:3]

input_shape = x_train[0].shape
output_shape = y_train.shape
batch_size = len(y_train)

model = tf.keras.models.Sequential()
model.add(Masking(mask_value=-1, input_shape=input_shape))
model.add(LSTM(batch_size, dropout=0.2))
model.add(Dense(input_dim=batch_size, units=output_shape[-1]))
model.add(Activation('softmax'))

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=100, batch_size=batch_size)
model.evaluate(x_test, y_test)

for text in ["Karibu kenya", ]:
    tokenized_text = tokenize(text)
    prediction = model.predict(tokenized_text, batch_size=1, verbose=1)

    # Results
    print("Text: {}: Prediction: {}".format(text, prediction))

其余代码似乎运行良好，但我无法运行model.predict(tokenized_text, batch_size=1, verboze=1)

我收到以下错误：

Epoch 97/100
19/19 [==============================] - 0s 196us/sample - loss: 0.8753 - accuracy: 0.5789
Epoch 98/100
19/19 [==============================] - 0s 246us/sample - loss: 0.8525 - accuracy: 0.6842
Epoch 99/100
19/19 [==============================] - 0s 169us/sample - loss: 0.7961 - accuracy: 0.6842
Epoch 100/100
19/19 [==============================] - 0s 191us/sample - loss: 0.7745 - accuracy: 0.7368
3/3 [==============================] - 0s 115ms/sample - loss: 0.5518 - accuracy: 1.0000
Traceback (most recent call last):
  File "start.py", line 65, in <module>
    prediction = model.predict(tokenized_text, batch_size=1, verbose=1)
  File "/home/felix/Projects/keras/.env/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 821, in predict
    use_multiprocessing=use_multiprocessing)
  File "/home/felix/Projects/keras/.env/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 705, in predict
    x, check_steps=True, steps_name='steps', steps=steps)
  File "/home/felix/Projects/keras/.env/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 2428, in _standardize_user_data
    exception_prefix='input')
  File "/home/felix/Projects/keras/.env/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_utils.py", line 512, in standardize_input_data
    'with shape ' + str(data_shape))
ValueError: Error when checking input: expected masking_input to have 3 dimensions, but got array with shape (1, 30)

不知道我做错了什么。我试图更改数据形状但仍然无法正常工作。

提前致谢。

标签： pythontensorflowkerasnlp

python - 如何在 Tensorflow 和 Keras 中正确建模 LSTM

问题描述

解决方案

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