[英]Using prediction from keras model as a layer inside another keras model
[英]Tensorflow: Use model inside another model as layer
我想在另一个 model 中使用分类 model 作为层,因为我认为 keras 模型也可以用作层。 这是第一个model的代码:
cencoder_inputs = keras.layers.Input(shape=[pad_len], dtype=np.int32)
ccondi_input = keras.layers.Input(shape=[1], dtype=np.int32)
ccondi_layer = tf.keras.layers.concatenate([cencoder_inputs, ccondi_input], axis=1)
cembeddings = keras.layers.Embedding(vocab_size, 4)
cencoder_embeddings = cembeddings(ccondi_layer)
clstm = keras.layers.LSTM(128)(cencoder_embeddings)
cout_layer = keras.layers.Dense(16, activation="softmax")(clstm)
classification_model = keras.Model(inputs=[cencoder_inputs, ccondi_input], outputs=[cout_layer])
classification_model.compile(optimizer="Nadam", loss="sparse_categorical_crossentropy", metrics=["accuracy"], experimental_run_tf_function=False)
我训练这个 model,保存并重新加载它为class_model并设置 trainable trainable=False
这是我的 model 的代码,它应该使用上面的 model 作为层:
encoder_inputs = keras.layers.Input(shape=[pad_len], dtype=np.int32)
decoder_inputs = keras.layers.Input(shape=[pad_len], dtype=np.int32)
condi_input = keras.layers.Input(shape=[1], dtype=np.int32)
class_layer = class_model((encoder_inputs, condi_input))
#Thats how I use the class model. Compilation goes fine so far
class_pred_layer = keras.layers.Lambda(lambda x: tf.reshape(tf.cast(tf.keras.backend.argmax(x, axis=1), dtype=tf.int32),shape=(tf.shape(encoder_inputs)[0],1)))(class_layer)
# Lambda and reshape layer, so I get 1 prediction per batch as integer
condi_layer = tf.keras.layers.concatenate([encoder_inputs, condi_input, class_pred_layer], axis=1)
embeddings = keras.layers.Embedding(vocab_size, 2)
encoder_embeddings = embeddings(condi_layer)
decoder_embeddings = embeddings(decoder_inputs)
encoder_1 = keras.layers.LSTM(64, return_sequences=True, return_state=True)
encoder_lstm_bidirectional_1 = keras.layers.Bidirectional(encoder_1)
encoder_output, state_h1, state_c1, state_h2, state_c2 = encoder_lstm_bidirectional_1(encoder_embeddings)
encoder_state = [Concatenate()([state_h1, state_h2]), Concatenate()([state_c1, state_c2])]
decoder_lstm = keras.layers.LSTM(64*2, return_sequences=True, return_state=True, name="decoder_lstm")
print(encoder_output.shape)
decoder_outputs,decoder_fwd_state, decoder_back_state = decoder_lstm(decoder_embeddings,initial_state=encoder_state)
print(decoder_outputs.shape)
attn_layer = AttentionLayer(name="attention_layer")
attn_out, attn_states = attn_layer([encoder_output, decoder_outputs])
decoder_concat_input = Concatenate(axis=-1, name="decoder_concat_layer")([decoder_outputs, attn_out])
decoder_dense_out = keras.layers.TimeDistributed(keras.layers.Dense(vocab_size, activation="softmax"))
decoder_outputs = decoder_dense_out(decoder_concat_input)
model = keras.Model(inputs=[encoder_inputs, decoder_inputs, condi_input], outputs=[decoder_outputs])
当我执行 model.fit() 时,我收到以下错误:
Inputs to eager execution function cannot be Keras symbolic tensors, but found [<tf.Tensor 'input_21:0' shape=(None, 35) dtype=int32>]
我认为训练有素的模型可以很容易地用作层,我做错了什么? 我也已经查看了这篇文章,但它也没有帮助我。 谢谢你的帮助!
好的,我会做两件事:(1)我会给你一个例子,我必须在另一个 model 中调用 model,以及(2)试着给你一个提示,说明你的问题可能在这里(我不能真正理解代码,但我过去有同样的错误)
1. 这是使用另一个 model 作为隐藏层的 model 示例:
def model_test(input_shape, sub_model):
inputs = Input(input_shape)
eblock_1_1 = dense_convolve(inputs, n_filters=growth_rate)
eblock_1_2 = dense_convolve(eblock_1_1, n_filters=growth_rate);
dblock_1_1 = dense_convolve(eblock_1_2, n_filters=growth_rate);
dblock_1_2 = dense_convolve(dblock_1_1, n_filters=growth_rate);
final_convolution = Conv3D(2, (1, 1, 1), padding='same', activation='relu')(dblock_1_2)
intermedio = sub_model(final_convolution)
layer = LeakyReLU(alpha=0.3)(intermedio)
model = Model(inputs=inputs, outputs=layer)
return model
我这样称呼它:
with strategy.scope():
sub_model = tf.keras.models.load_model('link_to_the_model')
sub_model.trainable = False
model = model_test(INPUT_SIZE, sub_model)
model.compile(optimizer=Adam(lr=0.1),
loss=tf.keras.losses.MeanSquaredError(),
metrics=None)
我刚刚在 google colab 上用 keras 测试了这个。
如果问题是 model 的调用可能会尝试做我所做的,将 model 作为参数传递,并在内部使用层作为参数调用它并将其用作简单层
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