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Implement adaHessian as an optimizer in neural network model

I want to use second order optimizer instead of using SGD, Adam, Adagrad, AdaDelta etc in neural network model. So I found AdaHessian as an optimizer. I have the following code:
# define model
model = Sequential()
model.add(Dense(132, input_dim=66, activation='linear'))
model.add(Dropout(0.5))
model.add(Dense(88, activation='linear'))
model.add(Dropout(0.5))
model.add(Dense(44, activation='linear'))
model.add(Dropout(0.5))
model.add(Dense(22, activation='linear'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mse', optimizer=optimizer = Adahessian(params=model), metrics=['accuracy'])
# fit model
model.fit(trainX, trainy, epochs=50, validation_split=0.2,verbose=0)
# evaluate the model
loss, test_acc = model.evaluate(testX, testy, verbose=0)
return model, loss, test_acc
Can anyone help me what params= means in AdaHessian() and what should I do to write i.e., model.parameters() or others? But I got error message:
AttributeError: 'Sequential' object has no attribute 'parameters'
Please show instructions so I can follow to implement second order optimizers in compilation of model.

TensorFlow Regression with EarlyStopping and Dropout results in underfitting

New to ML and I would like to know what I'm missing or doing incorrectly.
I'm trying to figure out why my data is being underfit when applying early stopping and dropout however when I don't use earlystopping or dropout the fit seems to be okay...
Dataset I'm using:
https://www.kaggle.com/datasets/kanths028/usa-housing
Model Parameters:
The dataset has 5 features to train on and the target is the price
I chose 4 layers arbitrarily
Epochs at 600 (way too many) because I want to test early stopping
Optimizers and loss because those seemed to get me the most consistent results when compared to SKLearns LinearRegression (MAE is about 81K)
Data Pre-preprocessing:
X = df[df.columns[:-2]].values
y = df['Price'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
Fit looks okay:
model = Sequential()
model.add(Dense(5, activation='relu'))
model.add(Dense(5, activation='relu'))
model.add(Dense(5, activation='relu'))
model.add(Dense(5, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mae')
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=600)
Data looks underfit with earlystopping and dropout combined:
model = Sequential()
model.add(Dense(10, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
early_stopping = EarlyStopping(monitor='val_loss', mode='min', patience=25)
model.compile(optimizer='adam', loss='mae')
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=600, callbacks=[early_stopping])
I'm trying to figure out why early stopping would stop when the results are so far off. I would guess that the model would continue until the end of the 600 epochs however early stopping pulls the plug around 300.
I'm probably doing something wrong but I can't figure it out so any insights would be appreciated. Thank you in advance :)

It defines performance measure and specifies whether to maximize or minimize it.
Keras then stops training at the appropriate epoch. When verbose=1 is designated, it is possible to output on the screen when the training is stopped in keras.
es = EarlyStopping(monitor='val_loss', mode='min')
It may not be effective to stop right away because performance does not increase. Patience defines how many times to allow epochs that do not increase performance. Partiance is a rather subjective criterion. The optimal value can be changed depending on the design of the used data and model used.
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50)
When the training is stopped by the Model Choice Early stopping object, the state will generally have a higher validation error than the previous model. Therefore, early stopping may be controlled so that the validation error of the model is no longer lowered by stopping the training of the model at a certain point in time, but the stopped state will not be the best model. Therefore, it is necessary to store the model with the best validation performance, and for this purpose, the object called Model Checkpoint exists in keras. This object monitors validation errors and unconditionally stores parameters at this time if the validation performance is better than the previous epoch. Through this, when training is stopped, the model with the highest validation performance can be returned.
from keras.callbacks import ModelCheckpoint
mc = ModelCheckpoint ('best_model.h5', monitor='val_loss', mode='min', save_best_only=True)
in the callbacks parameter, allowing the best model to be stored.
hist = model.fit(train_x, train_y, nb_epoch=10,
batch_size=10, verbose=2, validation_split=0.2,
callbacks=[early_stopping, mc])
In your case Patience 25 indicates whether to end when the reference value does not improve more than 25 times consecutively.
from keras.callbacks import ModelCheckpoint
model = Sequential()
model.add(Dense(10, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
early_stopping = EarlyStopping(monitor='val_loss', mode='min', patience=25, verbose=1)
mc = ModelCheckpoint ('best_model.h5', monitor='val_loss', mode='min', save_best_only=True)
model.compile(optimizer='adam', loss='mae')
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=600, callbacks=[early_stopping, mc])

I recommend 2 things. In the early stop callback set the parameter
restore_best_weights=True
This way if the early stopping callback activates, your model is set to the weights for the epoch with the lowest validation loss. To get the lower validation loss I recommend you use the callback ReduceLROnPlateau. My recommended code for these callbacks is shown below.
estop=tf.keras.callbacks.EarlyStopping( monitor="val_loss", patience=4,
verbose=1, estore_best_weights=True)
rlronp=tf.keras.callbacks.ReduceLROnPlateau(monitor="val_loss", factor=0.5,
patience=2, verbose=1)
callbacks=[estop, rlronp]
In model.fit set parameter callbacks=callbacks. Set epochs to a large number so it is likely the estop callback will be activated.

Keras EarlyCallBack stopping after one epoch

My dataset( Network traffic dataset where we do binary classification)-
Number of features is 25 and I have normalized the dataset.
My model-
verbose=1
epoch_number=1000
batch_size = 32
n_outputs = 1
model = Sequential()
model.add(Conv1D(filters=200, kernel_size=4, strides=3,activation='relu', input_shape=(25,1)))
model.add(Dropout(0.05))
model.add(BatchNormalization())
model.add(Conv1D(filters=200, kernel_size=5, strides=1,activation='relu', input_shape=(25,1)))
model.add(Dropout(0.05))
model.add(BatchNormalization())
model.add(MaxPooling1D(pool_size=2))
model.add(Dropout(0.05))
model.add(Flatten())
model.add(Dense(200, activation='relu'))
model.add(Dropout(0.05))
model.add(Dense(100, activation='relu'))
model.add(Dropout(0.05))
model.add(Dense(50, activation='relu'))
model.add(Dropout(0.05))
model.add(Dense(n_outputs, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam',metrics=['acc',f1_m,precision_m, recall_m])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1)
# fit network
model.fit(X_train, y_train,validation_data=(X_test, y_test),epochs=epoch_number, batch_size=batch_size, verbose=1,callbacks=[es])
# evaluate model
loss, accuracy, f1_score, precision, recall = model.evaluate(X_test, y_test, batch_size=batch_size, verbose=0)
print(loss,accuracy,f1_score,precision,recall)
My model is stopping after one epoch when I add Keras Earlycall back even though loss is decreasing after every epoch when I remove it.

If you had printed your logs of training of dataset without using early stopping then It would have been easier to diagnose.
Now Let's look at the possibilities. You have set EarlyStopping as mentioned below.
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1)
Then that means your early stopping layer is like mentioned below which has default parameters.
tf.keras.callbacks.EarlyStopping(
monitor="val_loss",
min_delta=0,
patience=0,
verbose=1,
mode="min",
baseline=None,
restore_best_weights=False,
)
Now here your patience=0 , mode='min', 'min_delta= 0' and monitor_loss = 'val_loss'
This simply means that if your validation loss is not decreasing in the next layer then it will stop.
Or if your Validation loss is same or greater than the previous epoch then it will stop.
I would recommend you to change your patience parameter

When using keras and trying to train my model on my GPU my program shuts down because "Integer value: x is too large". How do I fix this?

# Create the model
model = Sequential()
model.add(VGG16(weights="imagenet", include_top=False, input_shape=(IMG_SIZE, IMG_SIZE, 3)))
model.add(Flatten())
model.add(Dense(128, activation="relu"))
model.add(Dense(128, activation="relu"))
model.add(Dense(64, activation="relu"))
model.add(Dense(4, activation="sigmoid"))
model.layers[-6].trainable = False
model.summary()
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
train = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=50, batch_size=32, verbose=1)
I have borrowed some code and I am trying to train a model to read licence plates, but as stated in the title I get a integer than is out of bounds for int32. I do not get this problem if I remove a Cudnn library s.t the training is done on the CPU. If anyone recognizes the problem and know how to fix it that would be really helpful!
ps: I am using Windows 10, Visual Studio 2019 community, CUDA11.2, and the most recent Cudnn on a Intel and Nvidia RTX system

NN Accuracy Saturates After the Very First Epoch with Keras

I'm trying to fit a simple Neural Network to predict a binary target using keras-1.0.6. The output saturates after the very first epoch. I try playing around with the learning rate (from 0.1 to 1e-6), decay and momentum of the SGD optimizer and with the layers (10-512 hidden neurons and 1-2 hidden layers) and their activation functions of the network, but nothing worked - the prediction accuracy was the same.
My training set has shape (13602, 115) and my validation set has shape (3400,115). The target variable y_train and y_test have values encoded as 1 and 0 (60% are 1's and 40% are 0's). At first, the data was not normalized though when I normalized it I got the same results.
Verifying the output, I see that the model is predicting only 1 class. Sometimes it predicts only 1's and other times only 0's (when I tweak the model).
I also tried to encode the target variable in the shape (n_sample, 2) but the output was the same.
I followed some questions here and googling that suggests tunning the learning rate and not using 'softmax' activation but couldn't improve the results.
Some of the models I tried is below:
The simplest model:
model.add(Dense(1, input_dim=X_train.shape[1], activation='sigmoid'))
Model 2:
model = Sequential()
model.add(Dense(512, input_dim=X_train.shape[1]))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
model.add(Activation('sigmoid'))
Model 3
model.add(Dense(64, input_dim=X_train.shape[1], init='uniform', activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
Model 4
model.add(Dense(64, input_dim=X_train.shape[1], init='uniform', activation='sigmoid'))
model.add(Dense(1, input_dim=X_train.shape[1], activation='sigmoid'))
and to compile and fit the model:
sgd = SGD(lr=0.01, decay=0.1, momentum=0.0, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train2, nb_epoch=5, batch_size=50, validation_split=0.2)
model.predict(X_test)
The output gives either [0,0,0,0,0,0,0,...] or [1,1,1,1,1,1,1,1,...]
Does anybody have a clue on what's going on here?