CODEWITHSUNDEEP
pandasdeep-learningkeraskeras-layervalueerror
user8512104
user8512104

Reputation: 285

ValueError: expected dense_1_input to have shape (None, 4) but got (78,2)

I don't fundamentally understand the shapes of arrays or how to determine the epochs and batch sizes of training data. My data has 6 columns, column 0 is the independent variable - a string, columns 1-4 are the Deep Neural Network inputs and column 5 is the binary outcome due to the inputs. I have 99 rows of data. I want to understand how to get rid of this error.

#Importing Datasets
dataset=pd.read_csv('TestDNN.csv')
x = dataset.iloc[:,[1,5]].values # lower bound independent variable to upper bound in a matrix (in this case up to not including column5)
y = dataset.iloc[:,5].values # dependent variable vector
#Splitting data into Training and Test Data
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=0)


#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test=sc.transform(x_test)

# PART2 - Making ANN, deep neural network

#Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense

#Initialising ANN
classifier = Sequential()

#Adding the input layer and first hidden layer
classifier.add(Dense(activation= 'relu', input_dim =4, units=2, 
kernel_initializer="uniform"))#rectifier activation function
#Adding second hidden layer
classifier.add(Dense(activation= 'relu', units=2, 
kernel_initializer="uniform")) #rectifier activation function
#Adding the Output Layer
classifier.add(Dense(activation= 'sigmoid', units=1, 
kernel_initializer="uniform"))

#Compiling ANN - stochastic gradient descent
classifier.compile(optimizer='adam', loss='binary_crossentropy',metrics=
['accuracy'])

#Fit ANN to training set

#PART 3 - Making predictions and evaluating the model

#Fitting classifier to the training set
classifier.fit(x_train, y_train, batch_size=32, epochs=5)#original batch is 
10 and epoch is 100

Upvotes: 0

Views: 189

Answers (1)

Maxim
Maxim

Reputation: 53768

The problem is with x definition. This line:

x = dataset.iloc[:,[1,5]].values

... tells pandas to take the columns 1 and 5 only, so it has shape [78, 2]. You probably meant taking all columns before the 5-th:

x = dataset.iloc[:,:5].values

Upvotes: 1

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