[英]How to call functions of the class Sequential() if those are not in the source code?
我是機器學習的新手,我正在嘗試在keras中實現我的自定義層。 我找到了幾個教程,看起來比較直接。 但我不明白的是如何在Sequential()中實現我的新自定義圖層。 例如,我從tensorflow網站( https://www.tensorflow.org/tutorials/keras/basic_text_classification )上看到了這個分類問題,為方便起見,貼在這里:
from __future__ import absolute_import, division, print_function
import tensorflow as tf
from tensorflow import keras
import numpy as np
imdb = keras.datasets.imdb
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
# A dictionary mapping words to an integer index
word_index = imdb.get_word_index()
# The first indices are reserved
word_index = {k:(v+3) for k,v in word_index.items()}
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2 # unknown
word_index["<UNUSED>"] = 3
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
def decode_review(text):
return ' '.join([reverse_word_index.get(i, '?') for i in text])
train_data = keras.preprocessing.sequence.pad_sequences(train_data,
value=word_index["<PAD>"],
padding='post',
maxlen=256)
test_data = keras.preprocessing.sequence.pad_sequences(test_data,
value=word_index["<PAD>"],
padding='post',
maxlen=256)
# input shape is the vocabulary count used for the movie reviews (10,000 words)
vocab_size = 10000
model = keras.Sequential()
model.add(keras.layers.Embedding(vocab_size, 16))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(16, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))
model.summary()
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['acc'])
x_val = train_data[:10000]
partial_x_train = train_data[10000:]
y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]
history = model.fit(partial_x_train,
partial_y_train,
epochs=40,
batch_size=512,
validation_data=(x_val, y_val),
verbose=1)
results = model.evaluate(test_data, test_labels)
print(results)
我是否必須更改keras.Sequential()的源代碼或者有一個簡單的方法嗎?
此外,查看Sequential()類的源代碼讓我想知道:我無法弄清楚'summary()','compile()','fit()'和'evaluate()'這樣的函數是怎樣的如果在這個類的源代碼中甚至沒有提供它們,則調用它們。 以下是Sequential()的源代碼:
Sequential
是模型,而不是圖層。
您提到的函數( summary
, compile
, fit
, evaluate
)在此處鏈接的Model類中實現,因為Sequential是Model的子類。
如果您正在編寫自定義圖層,則應該改為繼承Layer,而不是Model或Sequential。
您需要實現build
, call
和compute_output_shape
來創建自己的圖層。
from keras import backend as K
from keras.layers import Layer
class MyLayer(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(MyLayer, self).__init__(**kwargs)
def build(self, input_shape):
# Create a trainable weight variable for this layer.
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[1], self.output_dim),
initializer='uniform',
trainable=True)
super(MyLayer, self).build(input_shape) # Be sure to call this at the end
def call(self, x):
return K.dot(x, self.kernel)
def compute_output_shape(self, input_shape):
return (input_shape[0], self.output_dim)
要使用它,請從您放入的文件中導入MyLayer類,然后像默認的Keras圖層一樣添加它:
from custom.layers import MyLayer
model = keras.Sequential()
model.add(MyLayer())
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