i have read a lot of information about several notions, like batch_size, epochs,iterations, but because of explanation was without numerical examples and i am not native speaker, i have some kind of problem of understanding still about those terms, so i decided to work with data and step by step show what i have done […]

- Tags "class_weight":None, #verbose=1, $iterations, 0].values X_train, 1:].values y =data.iloc[:, 3, 4) first of i have converted Gender using LabelEncoder to 0 and 1 le = preprocessing.LabelEncoder() data["Gender"] =le.fit_transform(data[", activation= "sigmoid")) model.add(Dense(1, activation= "sigmoid")) model.add(Dense(100, activation= "sigmoid")) model.add(Dense(50, activation='sigmoid')) model.compile(loss='categorical_crossentropy', and finally we have one output with sigmoid to convert result into 0 or 1. now let i trained model using following code model.fit(X_train, and i want to build neural network with will classify whether person with those attributes is female or male, batch_size=None, but because of explanation was without numerical examples and i am not native speaker, callbacks=None, could you please explain me with this data, epochs, epochs=1, epochs=30) by the way size of X_train is print(X_train.shape) -(177, first layer contains 500 node, i called basic sequential keras model and all necessary parameters import sklearn # Import necessary modules from sklearn.model_selection im, i have read a lot of information about several notions, i have some kind of problem of understanding still about those terms, initial_epoch=0, input_dim=3, it takes input three variable and on each node there is sigmoid activation function, like batch_size, max_queue_size=10, metrics=['accuracy']) Explanation : this model has three hidden layer, next layer contains 100 node and sigmoid activation, optimizer=adam, random_state=0) after that one, sample_weight=None, sheet_name='gender') size of data is : (237, shuffle=True, so i decided to work with data and step by step show what i have done and from there how to clarify this terms. let us suppose we have follow, so i used google colab and try following ones from google.colab import drive drive.mount('/content/drive') import pandas as pd import numpy, steps_per_epoch=None, stratify=y, third one contains 50 node and sigmoid again, use_multiprocessing=False, validation_data=None, validation_freq=1, validation_split=0.0, validation_steps=None, what does mean epochs=30? does it means that all 177 rows (with 3 input at times) will go to the model 30 times? what about batch_size ?becau, workers=1, x_test, y, y_test = train_test_split(x, y_train, y=None