调用层“顺序”时遇到异常

Question

我正在尝试使用 tensorflow 和 keras 构建文本生成器。 我正在使用 train.py 文件中的代码，该文件可在以下网站上找到：https://www.thepythoncode.com/code/text-generation-keras-python 。 以下是我正在运行的代码片段。 在这里，“inputz”是我用来训练 model 的文本字符串的变量名称：

import tensorflow as tf
import numpy as np
import os
import pickle
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout
from string import punctuation

sequence_length = 100
BATCH_SIZE = 128
EPOCHS = 30

# dataset file path
FILE_PATH = "data/EXAMPLE.txt"
BASENAME = os.path.basename(FILE_PATH)

# print some stats
n_chars = len(inputz)
vocab = ''.join(sorted(set(inputz)))
print("unique_chars:", vocab)
n_unique_chars = len(vocab)
print("Number of characters:", n_chars)
print("Number of unique characters:", n_unique_chars)

# dictionary that converts characters to integers
char2int = {c: i for i, c in enumerate(vocab)}
# dictionary that converts integers to characters
int2char = {i: c for i, c in enumerate(vocab)}

# save these dictionaries for later generation
pickle.dump(char2int, open(f"{BASENAME}-char2int.pickle", "wb"))
pickle.dump(int2char, open(f"{BASENAME}-int2char.pickle", "wb"))

# convert all text into integers
encoded_text = np.array([char2int[c] for c in inputz])

# construct tf.data.Dataset object
char_dataset = tf.data.Dataset.from_tensor_slices(encoded_text)

# print first 8 characters
for char in char_dataset.take(8):
    print(char.numpy(), int2char[char.numpy()])

# build sequences by batching
sequences = char_dataset.batch(2*sequence_length + 1, drop_remainder=True)

# print sequences
for sequence in sequences.take(2):
    print(''.join([int2char[i] for i in sequence.numpy()]))

def split_sample(sample):
    # example :
    # sequence_length is 10
    # sample is "python is a great pro" (21 length)
    # ds will equal to ('python is ', 'a') encoded as integers
    ds = tf.data.Dataset.from_tensors((sample[:sequence_length], sample[sequence_length]))
    for i in range(1, (len(sample)-1) // 2):
        # first (input_, target) will be ('ython is a', ' ')
        # second (input_, target) will be ('thon is a ', 'g')
        # third (input_, target) will be ('hon is a g', 'r')
        # and so on
        input_ = sample[i: i+sequence_length]
        target = sample[i+sequence_length]
        # extend the dataset with these samples by concatenate() method
        other_ds = tf.data.Dataset.from_tensors((input_, target))
        ds = ds.concatenate(other_ds)
    return ds

# prepare inputs and targets
dataset = sequences.flat_map(split_sample)


def one_hot_samples(input_, target):
    # onehot encode the inputs and the targets
    # Example:
    # if character 'd' is encoded as 3 and n_unique_chars = 5
    # result should be the vector: [0, 0, 0, 1, 0], since 'd' is the 4th character
    return tf.one_hot(input_, n_unique_chars), tf.one_hot(target, n_unique_chars)

# print first 2 samples
for element in dataset.take(2):
    print("Input:", ''.join([int2char[np.argmax(char_vector)] for char_vector in element[0].numpy()]))
    print("Target:", int2char[np.argmax(element[1].numpy())])
    print("Input shape:", element[0].shape)
    print("Target shape:", element[1].shape)
    print("="*50, "\n")

# repeat, shuffle and batch the dataset
ds = dataset.repeat().shuffle(1024).batch(BATCH_SIZE, drop_remainder=True)

model = Sequential([
    LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True),
    Dropout(0.3),
    LSTM(256),
    Dense(n_unique_chars, activation="softmax"),
])

# define the model path
model_weights_path = f"results/{BASENAME}-{sequence_length}.h5"
model.summary()
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

# make results folder if does not exist yet
if not os.path.isdir("results"):
    os.mkdir("results")
# train the model
model.fit(ds, steps_per_epoch=(len(encoded_text) - sequence_length) // BATCH_SIZE, epochs=EPOCHS)
# save the model
model.save(model_weights_path)

当我运行此代码时，我遇到以下错误：

WARNING:tensorflow:Model was constructed with shape (None, 100, 42) for input KerasTensor(type_spec=TensorSpec(shape=(None, 100, 42), dtype=tf.float32, name='lstm_input'), name='lstm_input', description="created by layer 'lstm_input'"), but it was called on an input with incompatible shape (128, 100).
Traceback (most recent call last):
  File "c:\Users\leehs\Desktop\Python\Garfield\new_text_gen.py", line 127, in <module>
    model.fit(ds, steps_per_epoch=(len(encoded_text) - sequence_length) // BATCH_SIZE, epochs=EPOCHS)
  File "C:\Users\leehs\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler    
    raise e.with_traceback(filtered_tb) from None
  File "C:\Users\leehs\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\tensorflow\python\framework\func_graph.py", line 1147, in autograph_handler
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:

    File "C:\Users\leehs\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\keras\engine\training.py", line 946, in train_function  *   
        return step_function(self, iterator)
    File "C:\Users\leehs\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\keras\engine\training.py", line 935, in step_function  **   
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "C:\Users\leehs\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\keras\engine\training.py", line 928, in run_step  **        
        outputs = model.train_step(data)
    File "C:\Users\leehs\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\keras\engine\training.py", line 841, in train_step
        y_pred = self(x, training=True)
    File "C:\Users\leehs\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler  
        raise e.with_traceback(filtered_tb) from None
    File "C:\Users\leehs\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\keras\engine\input_spec.py", line 214, in assert_input_compatibility
        raise ValueError(f'Input {input_index} of layer "{layer_name}" '

    ValueError: Exception encountered when calling layer "sequential" (type Sequential).

    Input 0 of layer "lstm" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (128, 100)

    Call arguments received:
      • inputs=tf.Tensor(shape=(128, 100), dtype=int32)
      • training=True
      • mask=None

我可以更改什么来阻止此错误再次发生？

Answer 1

tf.keras.layers.LSTM期望输入：一个 3D 张量，形状为[batch, timesteps, feature] 。

在这种情况下，输入形状是 2D

input_shape=(sequence_length, n_unique_chars)

为您的输入形状添加额外的尺寸或添加批量大小。

工作代码片段

inputs = tf.random.normal([32, 10, 8])
lstm = tf.keras.layers.LSTM(4)
output = lstm(inputs)
print(output.shape)
(32,4)