[Python] AttributeError: 'Dense' object has no attribute 'output_shape' in ann_visualizer with...

`i have this error for Epoch 1/2 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 391ms/step - loss: 6272135168.0000 Epoch 2/2 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - loss: 6272133632.0000 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step Mean Squared Error: 7431671762.639743 Traceback (most recent call last):

File ~\anaconda3\Lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec exec(code, globals, locals)

File c:\users\mouli.spyder-py3\temp.py:27 ann_viz(model,title='Linear Regression')

File ~\anaconda3\Lib\site-packages\ann_visualizer\visualize.py:42 in ann_viz input_layer = int(str(layer.input_shape).split(",")[1][1:-1]);

AttributeError: 'Dense' object has no attribute 'input_shape'

for this

`

import numpy as np
import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
import pandas as pd`
`# Importing the dataset
dataset = pd.read_csv(r"Salary_Data (1).csv")
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = keras.Sequential([
keras.layers.Input(shape=(1,)),
keras.layers.Dense`(1)
])`
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(`your text`X_train, y_train, epochs=2, batch_size=32, verbose=1)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")`

# Visualize the data and the regression line

#Visualizing the neural network
from ann_visualizer.visualize import ann_viz
from graphviz `your text`import Source
ann_viz(model,title='Linear Regression')
graph_source=Source.from_file('network.gv')

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