如何将输入层链接到tensorflow-hub? [英] How to chain an input layer to tensorflow-hub?

问题描述

我想通过以下嵌入方式将文本分为2类: https://tfhub.dev/google/universal-sentence-encoder-multilingual/3

I want to classify text to 2 classes by using this embedding: https://tfhub.dev/google/universal-sentence-encoder-multilingual/3

我还想在嵌入后添加其他功能.所以我有两个输入:

And I also want to add additional features after the embedding. So I have two inputs:

import tensorflow          as tf
import tensorflow_hub      as tfh
import tensorflow_datasets as tfds
import tensorflow_text     as tft


hp = {
    'embedding':     'https://tfhub.dev/google/universal-sentence-encoder-multilingual/3' EMBEDDINGS['senm'],
    'units':         64, 
    'learning_rate': 1e-3,
    'dropout':       0.2,
    'layers':        2
}

textInput     = tf.keras.Input(shape=(1, ),  name = 'text', dtype = tf.string)
featuresInput = tf.keras.Input(shape=(36, ), name = 'features')

x = tfh.KerasLayer(hp.get('embedding'), dtype = tf.string, trainable = False)(textInput)
x = tf.keras.layers.concatenate([x, featuresInput])

for index in range(hp.get('layers')):
    x = tf.keras.layers.Dense(hp.get('units'), activation = 'relu')(x)
    x = tf.keras.layers.Dropout(hp.get('dropout'))(x)

output = tf.keras.layers.Dense(
    1,
    activation       = 'sigmoid',
    bias_initializer = tf.keras.initializers.Constant(INITIAL_BIAS) if INITIAL_BIAS else None
)(x)

model = tf.keras.Model(inputs = [textInput, featuresInput], outputs = output)
model.compile(
    optimizer = tf.keras.optimizers.Adam(lr = hp.get('learning_rate')),
    loss      = tf.keras.losses.BinaryCrossentropy(),
    metrics   = METRICS,
)

代码失败并显示错误:

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-17-61aed6f885c9> in <module>
     10 featuresInput = tf.keras.Input(shape=(36, ), name = 'features')
     11 
---> 12 x = tfh.KerasLayer(hp.get('embedding'), dtype = tf.string, trainable = False)(textInput)
     13 x = tf.keras.layers.concatenate([x, featuresInput])
     14 

~/.virtualenvs/python3/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
    920                     not base_layer_utils.is_in_eager_or_tf_function()):
    921                   with auto_control_deps.AutomaticControlDependencies() as acd:
--> 922                     outputs = call_fn(cast_inputs, *args, **kwargs)
    923                     # Wrap Tensors in `outputs` in `tf.identity` to avoid
    924                     # circular dependencies.

~/.virtualenvs/python3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args, **kwargs)
    263       except Exception as e:  # pylint:disable=broad-except
    264         if hasattr(e, 'ag_error_metadata'):
--> 265           raise e.ag_error_metadata.to_exception(e)
    266         else:
    267           raise

AssertionError: in user code:

    /home/e/.virtualenvs/python3/lib/python3.6/site-packages/tensorflow_hub/keras_layer.py:222 call  *
        result = f()
    /home/e/.virtualenvs/python3/lib/python3.6/site-packages/tensorflow/python/saved_model/load.py:486 _call_attribute  **
        return instance.__call__(*args, **kwargs)
    /home/e/.virtualenvs/python3/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:580 __call__
        result = self._call(*args, **kwds)
    /home/e/.virtualenvs/python3/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:650 _call
        return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds)  # pylint: disable=protected-access
    /home/e/.virtualenvs/python3/lib/python3.6/site-packages/tensorflow/python/eager/function.py:1665 _filtered_call
        self.captured_inputs)
    /home/e/.virtualenvs/python3/lib/python3.6/site-packages/tensorflow/python/eager/function.py:1759 _call_flat
        "StatefulPartitionedCall": self._get_gradient_function()}):
    /usr/lib/python3.6/contextlib.py:81 __enter__
        return next(self.gen)
    /home/e/.virtualenvs/python3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py:4735 _override_gradient_function
        assert not self._gradient_function_map

    AssertionError: 

但是如果我使用顺序的，它会起作用

BUT it works if I use Sequential

    model = tf.keras.Sequential([
        hub.KerasLayer(embedding, input_shape=[], dtype = tf.string, trainable = True),
        tf.keras.layers.Dense(16, activation = 'relu', input_shape = (train_features.shape[-1],)),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(1, activation = 'sigmoid', bias_initializer = output_bias),
    ])

    model.compile(optimizer = tf.keras.optimizers.Adam(lr=1e-3), loss = tf.keras.losses.BinaryCrossentropy(), metrics = metrics)

函数API是否有做错什么?您能帮我解决这个错误吗

Is there anything I'm doing wrong with the functual api? Can you please help me with the error

推荐答案

我遇到了类似的问题.我的解决方案如下所示:

I've faced a similar problem. My solution looks like this:

def build_model():
    premise = keras.Input(shape=(), dtype=tf.string)
    hypothesis = keras.Input(shape=(), dtype=tf.string)
    keras_emb = hub.KerasLayer(embed, input_shape=(), output_shape = (512), dtype=tf.string, trainable=True)
    prem_emb = keras_emb(premise)
    hyp_emb = keras_emb(hypothesis)
    emb = layers.Concatenate()([prem_emb, hyp_emb])
    dense = layers.Dense(32, activation="relu")(emb)
    classifier = layers.Dense(3)(dense)
    model = keras.Model(inputs=[premise, hypothesis], outputs=classifier, name="elementary_model")
    model.compile(loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True), optimizer="adam", metrics=['accuracy'])
    return model

注意:文本输入形状应为()(空元组)

Note: the text input shape should be () (empty tuple)

这篇关于如何将输入层链接到tensorflow-hub?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！