Error while doing reshape

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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)

I got this error:

ValueError Traceback (most recent call 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 # Prepare validation data. 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, # Don't enforce the batch size. --> 749 exception_prefix='input') 750 751 if y is not None:

~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix) 125 ': expected ' + names[i] + ' to have ' + 126 str(len(shape)) + ' dimensions, but got array ' --> 127 'with shape ' + str(data_shape)) 128 if not check_batch_axis: 129 data_shape = data_shape[1:]

ValueError: Error when checking input: expected lstm_42_input to have 3 dimensions, but got array with shape (4, 11)

keras reshape machine-learning python
1 Answers

in the code is a problem with reshaping data. For reshaping cf Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (339732, 29) and https://github.com/keras-team/keras/issues/5214. In an python array the number of [ and ] indicates the dimensionality of an array

TimeDistributed layer needs at least two timesteps in your code you have one, in the code below timestep=3 is used because 33 is not divisible by 2. In general TimeDistributed layer is used to realize one-to-many and many-to-many configurations, cf https://github.com/keras-team/keras/issues/1029

the following code is working, the reshaping is done for 1 sample (batch_size), 3 timesteps, 11 features :

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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