In my code, I have an array (820,53). I'm trying to do a 1D convolution but I always get the following error: Error when checking input: expected conv1d_1_input to have 3 dimensions, but got array with shape (820, 53). How can I solve the problem?
from __future__ import print_function
from sklearn.preprocessing import LabelBinarizer, LabelEncoder
from keras.layers import Dense, Dropout, Activation
import numpy as np
import csv
from keras.models import Sequential
from keras.utils.vis_utils import plot_model
import random
from keras.layers import Conv1D, GlobalMaxPooling1D
from keras.layers import Embedding
# Funcao para centralizar os dados.
def center(mtx):
media = mtx.mean(axis=0)
Mtx_centr = mtx - media
return Mtx_centr
# Funcao para normalizar os dados.
def normalize(MTX):
normalizado = MTX.std(axis=0)
normalizado[normalizado == 0] = 1
MT_centr = center(MTX)
MT_normal = MT_centr / normalizado
return MT_normal
# Formatacao do arquivo CSV.
def csvread(filename, delimiter='\t'):
f = open(filename, 'r')
reader = csv.reader(f, delimiter=delimiter)
ncol = len(next(reader))
nfeat = ncol - 1
f.seek(0)
x = np.zeros(nfeat)
X = np.empty((0, nfeat))
y = []
for row in reader:
for j in range(nfeat):
x[j] = float(row[j])
X = np.append(X, [x], axis=0)
label = row[nfeat]
y.append(label)
lb = LabelBinarizer()
Y = lb.fit_transform(y)
classname = lb.classes_
le = LabelEncoder()
ynum = le.fit_transform(y)
return X, ynum
# Leitura do arquivo CSV.
def read_arq(A):
filename = A
delimiter = '\t'
X1, ynum = csvread(filename=filename, delimiter=delimiter)
X1 = normalize(X1)
std = X1.std(axis=0)
return X1, ynum
X_train, Y_train = read_arq('treino.csv') # Arquivo contendo os estados normais e com falhas para treino.
X_test, Y_test = read_arq('teste.csv') # Arquivo contendo os estados normais e com falhas para teste.
# Embaralhamento das caracteristicas.
newMatrix = np.zeros((X_train.shape))
cols =(np.arange(X_train.shape[1]))
random.shuffle(newMatrix)
for i in range(len(X_train)):
for j in range(len(X_train[0])):
newMatrix[i][j] = X_train[i][cols[j]]
model = Sequential()
model.add(Conv1D(25, 3, padding='valid', activation='relu', strides=1, input_shape= X_train.shape))
model.add(GlobalMaxPooling1D())
model.add(Dense(25))
model.add(Dropout(0.2))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, Y_train, epochs=2, batch_size=32, validation_data=(X_test, Y_test))
print(model.summary())