CODEWITHSUNDEEP
pythonkerasneural-networkconv-neural-networklstm
bashar
bashar

Reputation: 165

Using conv1D “Error when checking input: expected conv1d_input to have 3 dimensions, but got array with shape (213412, 36)”

My input is simply a csv file with 237124 rows and 37 columns :

I am trying to train my data on the conv1D model.

I have tried to build a CNN with one layer, but I have some problems with it.

The compiler outputs:

ValueError:Error when checking input: expected conv1d_9_input to have shape (213412, 36) but got array with shape (36, 1)

Code:

import pandas as pd
import numpy as np
import sklearn
from sklearn import metrics
from sklearn.model_selection import KFold
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.layers import Conv2D,Conv1D, MaxPooling2D,MaxPooling1D
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Dropout,BatchNormalization

dataset=pd.read_csv("C:/Users/User/Desktop/data.csv",encoding='cp1252')

dataset.shape
#output: (237124, 37)

array = dataset.values
X = array[:,0:36]
Y = array[:,36]

kf = KFold(n_splits=10)
kf.get_n_splits(X)

for trainindex, testindex in kf.split(X):
Xtrain, Xtest = X[trainindex], X[testindex]
Ytrain, Ytest = Y[trainindex], Y[testindex]

Xtrain.shape[0]
#output: 213412

Xtrain.shape[1]
#output: 36

Ytrain.shape[0]
#output: 213412

n_timesteps, n_features, n_outputs =Xtrain.shape[0], Xtrain.shape[1], 
Ytrain.shape[0]

model = Sequential()
model.add(Conv1D(filters=64, kernel_size=1, 
activation='relu',input_shape=(n_timesteps,n_features)))

model.add(Conv1D(filters=64, kernel_size=1, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=  
['accuracy'])
# fit network
model.fit(Xtrain, Ytrain, epochs=10, batch_size=32, verbose=0)

# Testing CNN model BY X test

Predictions = model.predict(Xtest,batch_size =100)
rounded = [round(x[0]) for x in Predictions]
Y_predection = pd.DataFrame(rounded)
Y_predection = Y_predection.iloc[:, 0]

.
.
.

I tried to modify the code this way: 

Xtrain = np.expand_dims(Xtrain, axis=2)

But the error remains the same.

Upvotes: 6

Views: 6044

Answers (2)

thushv89
thushv89

Reputation: 11333

There's a couple of problems I notice with your code.

  • Xtrain - Needs to be a 3D tensor. Because anything else, Conv1D cannot process. So if you have 2D data you need to add a new dimension to make it 3D.
  • Your input_shape needs to be changed to reflect that. For example, if you added only a single channel, it should be [n_features, 1].
# Here I'm assuming some dummy data
# Xtrain => [213412, 36, 1] (Note that you need Xtrain to be 3D not 2D - So we're adding a channel dimension of 1)
Xtrain = np.expand_dims(np.random.normal(size=(213412, 36)),axis=-1)
# Ytrain => [213412, 10]
Ytrain = np.random.choice([0,1], size=(213412,10))

n_timesteps, n_features, n_outputs =Xtrain.shape[0], Xtrain.shape[1], Ytrain.shape[1]

model = Sequential()
model.add(Conv1D(filters=64, kernel_size=1, 
activation='relu',input_shape=(n_features,1)))

model.add(Conv1D(filters=64, kernel_size=1, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit network
model.fit(Xtrain, Ytrain, epochs=10, batch_size=32, verbose=0)

Upvotes: 7

Igna
Igna

Reputation: 1127

You need to specifi only how many dimension X has, not how many samples you will pass for the input layer.

 model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_features,)))

This means that the input will be N samples of shape n_features

For the last layer you should change the number of units to how many classes you have instead of how many rows your data has.

Upvotes: -1

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