How to do it...

In the previous recipe, we built a model with a batch size of 32. In this recipe, we will go ahead and implement the model to contrast the scenario between a low batch size and a high batch size for the same number of epochs:

1. Preprocess the dataset and fit the model as follows:

(X_train, y_train), (X_test, y_test) = mnist.load_data()
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')
X_train = X_train/255
X_test = X_test/255
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
model = Sequential()
model.add(Dense(1000,input_dim=784,activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=30000, verbose=1)

Note that the only change in code is the batch_size parameter in the model fit process.

1. Plot the training and test accuracy and loss values over different epochs (the code to generate the following plots remains the same as the code we used in step 8 of the Training a vanilla neural network recipe):

In the preceding scenario, you should notice that the model accuracy reached ~98% at a much later epoch, when compared to the model accuracy it reached when the batch size was smaller.