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重塑时出错

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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（最近一次调用last）in（）53 X，y = ele（）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，verbose，callbacks，validation_split，validation_data，shuffle，class_weight ，sample_weight，initial_epoch，steps_per_epoch，validation_steps，** kwargs）948 sample_weight = sample_weight，949 class_weight = class_weight， - > 950 batch_size = batch_size）951＃准备验证数据 . 952 do_validation = False〜\ Anaconda3 \ lib \ site-packages \ keras \ engine \ training.py in _standardize_user_data（self，x，y，sample_weight，class_weight，check_array_lengths，batch_size）747 feed_input_shapes，748 check_batch_axis = False，＃Do not强制执行批量大小 . - > 749 exception_prefix ='input'）750 751如果y不是None：〜\ Anaconda3 \ lib \ site-packages \ keras \ engine \ training_utils.py in standardize_input_data（data，names，shapes，check_batch_axis，exception_prefix）125' ：期望'names [i]'具有'126 str（len（shape））'维度，但得到数组' - > 127'，形状'str（data_shape））128如果不是check_batch_axis：129 data_shape = data_shape [1 ：] ValueError：检查输入时出错：期望lstm_42_input有3个维度，但得到的形状为数组（4,11）

python machine-learning keras reshape

1 回答

  • 1

    在代码中是重塑数据的问题 . 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 ] 的数量表示数组的维数

    TimeDistributed 图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 timestep=3 因为33不能被2整除 . 通常 TimeDistributed 图层用于实现一对多和多对多配置， cf https://github.com/keras-team/keras/issues/1029

    以下代码正在运行，重新整形是针对1个样本（batch_size），3个步骤，11个特征完成的：

    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)
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