import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from keras import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical
from sklearn.model_selection import train_test_split
df = pd.read_csv("data\heart.csv")
print(df.head())
print(df.info())
print(df.describe())
input = df[[ "Sex" ,str("Age") ,"RestingBP" ,"Cholesterol"]]
output = df[['HeartDisease']]
input_train ,input_test ,output_train ,output_test =
train_test_split(input,output,test_size=0.2,random_state=9)
print("Number of rows in input_train:", input_train.shape[0])
print("Number of rows in input_test:", input_test.shape[0])
print("Number of rows in output_train", output_train.shape[0])
print("Number of rows in output_test", output_test.shape[0])
pridictor = Sequential()
pridictor.add(Dense(16, activation='relu', input_dim=4))
pridictor.add(Dense(16, activation='relu' ))
pridictor.add(Dense(16, activation='relu' ))
pridictor.add(Dense(2, activation='relu'))
pridictor.compile(Dense(optimizer='adam', loss='categorical_crossentropy',metrics=
['accuracy']))
so the error i am getting is init() missing 1 required positional argument: 'units' this error is coming from the last line of my code which is "pridictor.compile(Dense(optimizer='adam', loss='categorical_crossentropy',metrics= ['accuracy']))"
CodePudding user response:
The problem comes from the line pridictor.compile(Dense(optimizer='adam', loss='categorical_crossentropy', metrics= ['accuracy']))
.
It seems you are trying to compile your model wrongly. To compile your model you can do: pridictor.compile(optimizer="Adam", loss="categorical_crossentropy", metrics=["accuracy"])
.
Please notice the omission of Dense in the syntax, pridictor.compile()
function requires the optimizer
and loss
arguments, but instead you are providing a Dense layer as an argument, which is wrong. You can see the documentation here if you want to know more.
CodePudding user response:
Compile is not a layer. It optimise your network with a optimiser function which in your case is Adam. You could remove "dense" from pridictor.compile and that should run.