How feed a numpy array in batches in Keras

You should implement a generator and feed it to model.fit_generator().

Your generator may look like this:

def batch_generator(X, Y, batch_size = BATCH_SIZE):
    indices = np.arange(len(X)) 
    batch=[]
    while True:
            # it might be a good idea to shuffle your data before each epoch
            np.random.shuffle(indices) 
            for i in indices:
                batch.append(i)
                if len(batch)==batch_size:
                    yield X[batch], Y[batch]
                    batch=[]

And then, somewhere in your code:

train_generator = batch_generator(trainX, trainY, batch_size = 64)
model.fit_generator(train_generator , ....)

UPD.: I order to avoid placing all your data into memory beforehand, you can modify the generator to consume only the identifiers of your data-set and then load your data on-demand:

def batch_generator(ids, batch_size = BATCH_SIZE):
    batch=[]
    while True:
            np.random.shuffle(ids) 
            for i in ids:
                batch.append(i)
                if len(batch)==batch_size:
                    yield load_data(batch)
                    batch=[]

Your loader function may look like this:

def load_data(ids):
   X = []
   Y = []

   for i in ids:
     # read one or more samples from your storage, do pre-processing, etc.
     # for example:
     x = imread(f'image_{i}.jpg')
     ...
     y = targets[i]

     X.append(x)
     Y.append(y)

   return np.array(X), np.array(Y)

Another approach using Keras Sequence class:

class DataGenerator(keras.utils.Sequence):
  def __init__(self, x_data, y_data, batch_size):
    self.x, self.y = x_data, y_data
    self.batch_size = batch_size
    self.num_batches = np.ceil(len(x_data) / batch_size)
    self.batch_idx = np.array_split(range(len(x_data)), self.num_batches)

  def __len__(self):
    return len(self.batch_idx)

  def __getitem__(self, idx):
    batch_x = self.x[self.batch_idx[idx]]
    batch_y = self.y[self.batch_idx[idx]]
    return batch_x, batch_y

train_generator = DataGenerator(x_train, y_train, batch_size = 128)
model.fit(train_generator,...)

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