[Python] Problem with fitting a neural network model on binary classification data

I have a dataset of 280 samples with 20 features and 0,1 results. I scaled them.

Also, having three neural network models to train data on them.

But while looping on models I receive an error with fitting.

How to resolve it?

import numpy as np
import math

import tensorflow as tf

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler,PolynomialFeatures
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from tensorflow.keras.losses import binary_crossentropy
from tensorflow.keras.optimizers import Adam


from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense


model1 = Sequential([
Dense(units=25, activation='relu'),
Dense(units=15, activation='relu'),
Dense(units=1, activation='linear')
])
model2 = Sequential([
Dense(units=20, activation='relu'),
Dense(units=12, activation='relu'),
Dense(units=12, activation='relu'),
Dense(units=20, activation='relu'),
Dense(units=1, activation='linear')
])
model3 = Sequential([
Dense(units=32, activation='relu'),
Dense(units=16, activation='relu'),
Dense(units=8, activation='relu'),
Dense(units=4, activation='relu'),
Dense(units=12, activation='relu'),
Dense(units=1, activation='linear')
])


nn_train_error = []
nn_cv_error = []

models_bc = [model1, model2, model3]
for model in models_bc:

# setup the loss and optimizer
model.compile(
loss = tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
)

print(f"Training {model.name}...")
# train the model
model.fit(x_bc_train_scaled, y_bc_train, epochs=100, verbose=0)
print("Done!\n")

# Set the threshold for classification
threshold = 0.5

# Record the fraction of misclassified examples for the training set
yhat = model.predict(x_bc_train_scaled)
yhat = tf.math.sigmoid(yhat)
yhat = np.where(yhat >= threshold, 1, 0) # where in np : condition function
train_error = np.mean(yhat != y_bc_train) # mean in np : show the percent of misclassified

nn_train_error.append(train_error)

# Record the fraction of misclassified examples for the cross validation set
yhat = model.predict(x_bc_cv_scaled)
yhat = tf.math.sigmoid(yhat)
yhat = np.where(yhat >= threshold, 1, 0)
cv_error = np.mean(yhat != y_bc_cv)

nn_cv_error.append(cv_error)

print(nn_train_error)
print(nn_cv_error)


output:

ValueError Traceback (most recent call
last) Cell In\[109\], line 15 13 print(f"Training
{model.name}...") 14 # train the model ---\> 15
model.fit(x_bc_train_scaled, y_bc_train, epochs=100, verbose=0)
16 print("Done!\\n")


Arguments received by Sequential.call():

• inputs=tf.Tensor(shape=(None, 20), dtype=float32)
• training=True
• mask=None

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