我一直在用keras训练此模型以用作文本生成器
# Small LSTM Network to Generate Text for Alice in Wonderland
import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils
# load ascii text and covert to lowercase
filename = "lyrics_small.txt"
raw_text = open(filename).read()
raw_text = raw_text.lower()
# create mapping of unique chars to integers
chars = sorted(list(set(raw_text)))
char_to_int = dict((c, i) for i, c in enumerate(chars))
# summarize the loaded data
n_chars = len(raw_text)
n_vocab = len(chars)
print ("Total Characters: ", n_chars)
print ("Total Vocab: ", n_vocab)
# prepare the dataset of input to output pairs encoded as integers
seq_length = 100
dataX = []
dataY = []
for i in range(0, n_chars - seq_length, 1):
seq_in = raw_text[i:i + seq_length]
seq_out = raw_text[i + seq_length]
dataX.append([char_to_int[char] for char in seq_in])
dataY.append(char_to_int[seq_out])
n_patterns = len(dataX)
print ("Total Patterns: ", n_patterns)
# reshape X to be [samples, time steps, features]
X = numpy.reshape(dataX, (n_patterns, seq_length, 1))
# normalize
X = X / float(n_vocab)
# one hot encode the output variable
y = np_utils.to_categorical(dataY)
model = Sequential()
model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(256))
model.add(Dropout(0.2))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
# define the checkpoint
filepath="weights-improvement-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(X, y, epochs=20, batch_size=128, callbacks=callbacks_list)
一旦完成运行，我意识到它实际上并没有保存模型。
没有问题，因为您可以将权重加载到模型中并使用它，并且有关此博客的说明中对此有说明
# load ascii text and covert to lowercase
filename = "lyrics_small.txt"
raw_text = open(filename).read()
raw_text = raw_text.lower()
# create mapping of unique chars to integers
chars = sorted(list(set(raw_text)))
char_to_int = dict((c, i) for i, c in enumerate(chars))
# summarize the loaded data
n_chars = len(raw_text)
n_vocab = len(chars)
print ("Total Characters: ", n_chars)
print ("Total Vocab: ", n_vocab)
# prepare the dataset of input to output pairs encoded as integers
seq_length = 100
dataX = []
dataY = []
for i in range(0, n_chars - seq_length, 1):
seq_in = raw_text[i:i + seq_length]
seq_out = raw_text[i + seq_length]
dataX.append([char_to_int[char] for char in seq_in])
dataY.append(char_to_int[seq_out])
n_patterns = len(dataX)
print ("Total Patterns: ", n_patterns)
# reshape X to be [samples, time steps, features]
X = numpy.reshape(dataX, (n_patterns, seq_length, 1))
# normalize
X = X / float(n_vocab)
# one hot encode the output variable
y = np_utils.to_categorical(dataY)
model = Sequential()
model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(256))
model.add(Dropout(0.2))
model.add(Dense(y.shape[1], activation='softmax'))
# load the network weights
filename = "weights-improvement-17-1.7150.hdf5"
model.load_weights(filename)
model.compile(loss='categorical_crossentropy', optimizer='adam')
# pick a random seed
start = numpy.random.randint(0, len(dataX)-1)
pattern = dataX[start]
print ("Seed:")
print ("\"", ''.join([int_to_char[value] for value in pattern]), "\"")
# generate characters
for i in range(1000):
x = numpy.reshape(pattern, (1, len(pattern), 1))
x = x / float(n_vocab)
prediction = model.predict(x, verbose=0)
index = numpy.argmax(prediction)
result = int_to_char[index]
seq_in = [int_to_char[value] for value in pattern]
sys.stdout.write(result)
pattern.append(index)
pattern = pattern[1:len(pattern)]
print ("\nDone.")
但是当我运行模型时，它引发了不兼容错误
Using TensorFlow backend.
Total Characters: 1571440
Total Vocab: 124
Total Patterns: 1571340
Traceback (most recent call last):
File "/home/sam/code/envs/data/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1628, in _create_c_op
c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimension 1 in both shapes must be equal, but are 124 and 121. Shapes are [256,124] and [256,121]. for 'Assign_6' (op: 'Assign') with input shapes: [256,124], [256,121].
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "new_model_trained.py", line 58, in <module>
model.load_weights(filename)
File "/home/sam/code/envs/data/lib/python3.6/site-packages/keras/engine/network.py", line 1166, in load_weights
f, self.layers, reshape=reshape)
File "/home/sam/code/envs/data/lib/python3.6/site-packages/keras/engine/saving.py", line 1058, in load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples)
File "/home/sam/code/envs/data/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2465, in batch_set_value
assign_op = x.assign(assign_placeholder)
File "/home/sam/code/envs/data/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 1718, in assign
name=name)
File "/home/sam/code/envs/data/lib/python3.6/site-packages/tensorflow/python/ops/state_ops.py", line 221, in assign
validate_shape=validate_shape)
File "/home/sam/code/envs/data/lib/python3.6/site-packages/tensorflow/python/ops/gen_state_ops.py", line 61, in assign
use_locking=use_locking, name=name)
File "/home/sam/code/envs/data/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/sam/code/envs/data/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/home/sam/code/envs/data/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
op_def=op_def)
File "/home/sam/code/envs/data/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1792, in __init__
control_input_ops)
File "/home/sam/code/envs/data/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1631, in _create_c_op
raise ValueError(str(e))
ValueError: Dimension 1 in both shapes must be equal, but are 124 and 121. Shapes are [256,124] and [256,121]. for 'Assign_6' (op: 'Assign') with input shapes: [256,124], [256,121].
因为模型是从input_shape的形状中提取出来的，所以X导致我假设模型没有以一致的方式分割内容。
有没有办法解决这个问题？
答案 0 :(得分：0)
您用于代码的数据集可能有所不同，您可以尝试更改此行：
y = np_utils.to_categorical(dataY)
对此：
np_utils.to_categorical(y_train, num_classes=121)
您可以像这样加载模型，但是在数据集不同时不要等待准确性