Question

所以我试图建立一个单词嵌入模型，但是我一直收到这个错误。在训练期间，准确性不会改变，并且val_loss仍为“ nan”

数据的原始形状为

x.shape, y.shape
((94556,), (94556, 2557))

然后我将其重塑为：

xr= np.asarray(x).astype('float32').reshape((-1,1))
yr= np.asarray(y).astype('float32').reshape((-1,1))
((94556, 1), (241779692, 1))

然后我通过模型运行它

model = Sequential()
model.add(Embedding(2557, 64, input_length=150, embeddings_initializer='glorot_uniform'))
model.add(Flatten())
model.add(Reshape((64,), input_shape=(94556, 1)))
model.add(Dense(512, activation='sigmoid'))
model.add(Dense(128, activation='sigmoid'))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='sigmoid'))
model.add(Dense(1, activation='relu'))
# compile the mode
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# summarize the model
print(model.summary())
plot_model(model, show_shapes = True, show_layer_names=False)

训练后，我得到了一个恒定的精度，并且每个时期都有val_loss nan

history=model.fit(xr, yr, epochs=20, batch_size=32, validation_split=3/9)

Epoch 1/20
WARNING:tensorflow:Model was constructed with shape (None, 150) for input Tensor("embedding_6_input:0", shape=(None, 150), dtype=float32), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 150) for input Tensor("embedding_6_input:0", shape=(None, 150), dtype=float32), but it was called on an input with incompatible shape (None, 1).
1960/1970 [============================>.] - ETA: 0s - loss: nan - accuracy: 0.9996WARNING:tensorflow:Model was constructed with shape (None, 150) for input Tensor("embedding_6_input:0", shape=(None, 150), dtype=float32), but it was called on an input with incompatible shape (None, 1).
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 2/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 3/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 4/20
1970/1970 [==============================] - 8s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 5/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 6/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 7/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 8/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 9/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 10/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 11/20
1970/1970 [==============================] - 8s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 12/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 13/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 14/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 15/20
1970/1970 [==============================] - 8s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 16/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 17/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 18/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 19/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996
Epoch 20/20
1970/1970 [==============================] - 7s 4ms/step - loss: nan - accuracy: 0.9996 - val_loss: nan - val_accuracy: 0.9996

我认为它必须做输入/输出形状，但我不确定。我尝试以各种方式修改模型，添加层/删除层/使用不同的优化器/使用不同的批处理大小，但到目前为止没有任何效果。

Answer 1

好的，这就是我的理解，如果我错了，请纠正我：

x包含94556个整数，每个整数是2557个单词中一个的索引。
y包含94556个2557个整数的向量，每个向量还包含一个单词的索引，但这一次是单次编码而不是分类编码。
最后，来自x和y的相应单词对表示原始文本中附近的两个单词。

如果到目前为止我是对的，那么以下内容可以正确运行：

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import *

x = np.random.randint(0,2557,94556)
y = np.eye((2557))[np.random.randint(0,2557,94556)]
xr = x.reshape((-1,1))


print("x.shape: {}\nxr.shape:{}\ny.shape: {}".format(x.shape, xr.shape, y.shape))


model = Sequential()
model.add(Embedding(2557, 64, input_length=1, embeddings_initializer='glorot_uniform'))
model.add(Reshape((64,)))
model.add(Dense(512, activation='sigmoid'))
model.add(Dense(2557, activation='softmax'))

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

history=model.fit(xr, y, epochs=20, batch_size=32, validation_split=3/9)

最重要的修改：

y重塑正在失去x和y中元素之间的关系。
input_length层中的Embedding应该与xr的第二维相对应。
网络最后一层的输出应与y的第二维相同。

我真的很惊讶代码没有崩溃就运行了。

最后，根据我的研究，似乎人们实际上并没有在训练这样的跳跃图，而是在试图预测训练示例是否正确（两个词在附近）。也许这就是您提出第一个维度输出的原因。

这是一个受https://github.com/PacktPublishing/Deep-Learning-with-Keras/blob/master/Chapter05/keras_skipgram.py启发的模型：

word_model = Sequential()
word_model.add(Embedding(2557, 64, embeddings_initializer="glorot_uniform", input_length=1))
word_model.add(Reshape((embed_size,)))

context_model = Sequential()
context_model.add(Embedding(2557, 64, embeddings_initializer="glorot_uniform", input_length=1))
context_model.add(Reshape((64,)))

model = Sequential()
model.add(Merge([word_model, context_model], mode="dot", dot_axes=0))
model.add(Dense(1, kernel_initializer="glorot_uniform", activation="sigmoid"))

在这种情况下，您将有3个向量，它们的大小都相同(94556, 1)（或者甚至可能大于94556，因为您可能必须生成其他负样本）：

x包含0到2556之间的整数
y包含0到2556之间的整数
output包含0和1，无论x和y中的每对都是阴性还是阳性示例

培训内容如下：

history = model.fit([x, y], output, epochs=20, batch_size=32, validation_split=3/9)

警告：警告：tensorflow：模型是用形状（无，150）构造的，但在形状不兼容（无，1）的输入上被调用

1 个答案: