I am confused about the batch size of this model. I have used sgd i.e., Stochastic Gradient Descent as the optimizer (see the code). I am aware that in sgd, a single random instance from the training set is used to compute the gradient at each step. So, according to it, the batch_size should be equal to 1. Now, in the tf.keras.Sequential.fit() documentation it says:
If unspecified, batch_size will default to 32.
So, do I have to manually set the batch_size equal to 1? It is because the default value, 32 will make it a Mini-batch Gradient Descent.
import tensorflow as tf from tensorflow import keras fashion_mnist = keras.datasets.fashion_mnist (X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data() X_valid, X_train = X_train_full[:5000]/255.0, X_train_full[5000:]/255.0 y_valid, y_train = y_train_full[:5000], y_train_full[5000:] model = keras.models.Sequential() model.add(keras.layers.InputLayer(input_shape = [28, 28])) model.add(keras.layers.Flatten()) model.add(keras.layers.Dense(300, activation = "relu")) model.add(keras.layers.Dense(100, activation = "relu")) model.add(keras.layers.Dense(10, activation = "softmax")) model.compile(loss = "sparse_categorical_crossentropy", optimizer = "sgd", metrics = ["accuracy"]) history = model.fit(X_train, y_train, epochs = 30, validation_data = (X_valid, y_valid))