重塑时出错

Question

from random import randint
from random import seed
import math
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense,TimeDistributed,RepeatVector

seed(1)
def ele():
    X,y = [],[]
    for i in range(1):
        l1=[]
        for _ in range(2):
            l1.append(randint(1,10))
        X.append(l1)
        y.append(sum(l1))
    for i in range(1):
        X = str(X[0][0])+'+'+str(X[0][1])
        y = str(y[0])
    char_to_int = dict((c, i) for i, c in enumerate(alphabet))
    Xenc,yenc = [],[]
    for pattern in X:
        integer_encoded = [char_to_int[char] for char in pattern]
        Xenc.append(integer_encoded[0])
    for pattern in y:
        integer_encoded = [char_to_int[char] for char in pattern]
        yenc.append(integer_encoded[0])
    k,k1 = [],[]
    for i in range(1):
        for j in Xenc:
            vec = np.zeros(11)
            vec[j] = 1
            k.append(vec)
        for j in yenc:
            vec1 = np.zeros(11)
            vec1[j] = 1
            k1.append(vec1)
        k = np.array(k)
        k1 = np.array(k1)
    return k,k1

alphabet = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+']

model = Sequential()
model.add(LSTM(100, input_shape=(n_in_seq_length,11)))
model.add(RepeatVector(2))
model.add(LSTM(50, return_sequences=True))
model.add(TimeDistributed(Dense(n_chars, activation='softmax')))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

for i in range(1):
    X,y = ele()
    #X = np.reshape(X, (4,1,11))
    model.fit(X, y, epochs=1, batch_size=10)

我收到了这个错误：

ValueError Traceback（最近调用 最后）在（） 第53章 54 #X = np.reshape(X, (4,1,11)) ---> 55 model.fit(X, y, epochs=1, batch_size=10)

~\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y，batch_size，epochs，详细，回调，validation_split， 验证数据，洗牌，类权重，样本权重，初始时期， steps_per_epoch, validation_steps, **kwargs) 第948章 第949章 --> 950 批次大小=批次大小） 951 # 准备验证数据。 第952章

~\Anaconda3\lib\site-packages\keras\engine\training.py 在 _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size) 第747章 748 check_batch_axis=False, #不强制批量大小。 --> 749 异常_前缀='输入') 750 751 如果 y 不是无：

~\Anaconda3\lib\site-packages\keras\engine\training_utils.py 在 标准化输入数据（数据，名称，形状，check_batch_axis， 异常前缀） 125 ': 预期 ' + names[i] + ' 有 ' + 126 str(len(shape)) + ' 维度，但得到了数组 ' --> 127 '带形状' + str(data_shape)) 128 如果不是 check_batch_axis： 129 数据形状=数据形状[1：]

ValueError：检查输入时出错：预期 lstm_42_input 有 3 维，但得到了形状为 (4, 11) 的数组

Answer 1

代码中存在重塑数据的问题。用于重塑 cf Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (339732, 29) 和 https://github.com/keras-team/keras/issues/5214。在python数组中[和]的个数表示数组的维数

TimeDistributed 层在您的代码中至少需要 两个 时间步长，在下面的代码中使用了 timestep=3，因为 33 不能被 2 整除。一般来说，TimeDistributed 层是用于实现一对多和多对多配置，参见https://github.com/keras-team/keras/issues/1029

以下代码正在运行，对1 个样本（batch_size）、3 个时间步、11 个特征进行了整形：

from random import randint
from random import seed
import math
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense,TimeDistributed,RepeatVector

seed(1)
def ele():
    X,y = [],[]
    for i in range(1):
        l1=[]
        for _ in range(2):
            l1.append(randint(1,10))
        X.append(l1)
        y.append(sum(l1))
    for i in range(1):
        X = str(X[0][0])+'+'+str(X[0][1])
        y = str(y[0])
    char_to_int = dict((c, i) for i, c in enumerate(alphabet))
    Xenc,yenc = [],[]
    for pattern in X:
        integer_encoded = [char_to_int[char] for char in pattern]
        Xenc.append(integer_encoded[0])
    for pattern in y:
        integer_encoded = [char_to_int[char] for char in pattern]
        yenc.append(integer_encoded[0])
    k,k1 = [],[]
    for i in range(1):
        for j in Xenc:
            vec = np.zeros(11)
            vec[j] = 1
            k.append(vec)
        for j in yenc:
            vec1 = np.zeros(11)
            vec1[j] = 1
            k1.append(vec1)
        k = np.array(k)
        k1 = np.array(k1)
    return k,k1




alphabet = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+']
n_chars = 11

for i in range(1):
    X,y = ele()
    print('X not reshaped :', X)
    print('y not reshaped :', y)

    # timestep = 3, batch_size =1, input_dim = nb_features = 11
    X = np.reshape(X, (1,X.shape[0],X.shape[1]))
    y = np.reshape(y, (1,y.shape[0],y.shape[1]))
    print('X reshaped :', X)
    print('y reshaped :', y)
    print(' X.shape[0] :', X.shape[0])
    print(' X.shape[1] :', X.shape[1])
    print(' X.shape[2] :', X.shape[2])

model = Sequential()
model.add(LSTM(100, input_shape=(X.shape[1],X.shape[2])))
model.add(RepeatVector(2))
model.add(LSTM(50, return_sequences=True))
model.add(TimeDistributed(Dense(n_chars, activation='softmax')))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

model.fit(X, y, epochs=1, batch_size=10)