NN Digit recognizition - Memory error, any ideas?

Question

im trying to make a trained OCR for digits, but i keep getting a memory allocation error and cant seem to figure out whats going wrong, worked just fine in start, but after 2 runs it suddenly started spiting these errors out, tried deleting zips and re downloading for possible corrupted files but with no results cant even complete first run anymore.

Alocation error:

Traceback (most recent call last):
  File "/home/pcname/PycharmProjects/Bot/venv/lib/python3.6/site-packages/theano/compile/function_module.py", line 607, in __call__
    outputs = self.fn()
MemoryError: Unable to allocate array with shape (60000, 800) and data type float64

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/pcname/PycharmProjects/Bot/Utility/TextHandler.py", line 90, in <module>
    train_err = train_fn(training_x, training_y)
  File "/home/pcname/PycharmProjects/Bot/venv/lib/python3.6/site-packages/theano/compile/function_module.py", line 618, in __call__
    storage_map=self.fn.storage_map)
  File "/home/pcname/PycharmProjects/Bot/venv/lib/python3.6/site-packages/theano/gof/link.py", line 269, in raise_with_op
    storage_map_list.sort(key=itemgetter(3), reverse=True)
TypeError: '<' not supported between instances of 'NoneType' and 'NoneType'

Code:

import os
import urllib.request
import gzip
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

matplotlib.use("TkAgg")
import lasagne
import theano
import theano.tensor as T

def build_nn(input_var=None):
        l_in = lasagne.layers.InputLayer(shape=(None, 1, 28, 28), input_var=input_var)
        l_in_drop = lasagne.layers.DropoutLayer(l_in, p=0.2)

        # Verborgen lagen.
        l_hid1 = lasagne.layers.DenseLayer(l_in_drop, num_units=800,
                                           nonlinearity=lasagne.nonlinearities.rectify,
                                           W=lasagne.init.GlorotUniform())

        l_hid1_drop = lasagne.layers.DropoutLayer(l_hid1, p=0.5)

        l_hid2 = lasagne.layers.DenseLayer(l_hid1_drop, num_units=800,
                                           nonlinearity=lasagne.nonlinearities.rectify,
                                           W=lasagne.init.GlorotUniform())

        l_hid2_drop = lasagne.layers.DropoutLayer(l_hid2, p=0.5)

        l_out = lasagne.layers.DenseLayer(l_hid2_drop, num_units=10,
                                          nonlinearity= lasagne.nonlinearities.softmax)

        return l_out


def load_dataset(file_path="../Lib/Zips/"):
    first_download = True
    def download(filename, source='http://yann.lecun.com/exdb/mnist/'):
        print("Downloading set: %s" % filename)
        urllib.request.urlretrieve(source+filename, file_path+filename)

    def load_imageset(file):
        # Indien het bestand nog niet gevonden is downloaden
        if not os.path.exists(file_path+file):
            download(file)

        # Openen van archief training set afbeeldingen.
        with gzip.open(file_path+file, 'rb') as reader:
            data = np.frombuffer(reader.read(), np.uint8, offset=16)
            data = data.reshape(-1, 1, 28, 28)

            return data/np.float32(256)

    def load_labelset(file):
        if not os.path.exists(file_path+file):
            download(file)

        with gzip.open(file_path+file, "rb") as reader:
            data = np.frombuffer(reader.read(), np.uint8, offset=8)

        return data

    training = ("train-images-idx3-ubyte.gz", "train-labels-idx1-ubyte.gz" )
    test = ("t10k-images-idx3-ubyte.gz", "t10k-labels-idx1-ubyte.gz")
    return load_imageset(training[0]),load_labelset(training[1]), load_imageset(test[0]), load_labelset(test[1])


x_train, y_train, x_test, y_test = load_dataset()


input_var = T.tensor4('inputs')
target_var = T.ivector('targets')

network = build_nn(input_var)

prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean()

params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.9)

theano.config.optimizer='fast_compile'
theano.config.exception_verbosity='high'
theano.config.compute_test_value = 'warn'

train_fn = theano.function([input_var, target_var], loss, updates=updates)
num_training_steps = 10

for step in range(num_training_steps):
    train_err = train_fn(x_train, y_train)

Answer 1

It is possible that your memory error is because you are trying to put all your data through the network at the same time.

your code:

for step in range(num_training_steps):
    train_err = train_fn(x_train, y_train)

The better way to train is by using mini-batches. Docs here: https://lasagne.readthedocs.io/en/latest/user/tutorial.html

To quickly experiment if you have enough memory for a small batch size, you could try replacing the above code with:

train_err = train_fn(x_train[0:8], y_train[0:8])

where 8 will simulate a batch size of 8. If the code runs without error, they you can be confident that once you go through the documentation link it will run fine.

What you will need to do is implement a generator function iterate_minibatches

And then train like so...

batch_size = 8 #you can edit
num_epochs = 4 #you can edit
for epoch in range(num_epochs):
    for batch in iterate_minibatches(x_train, y_train, batch_size, shuffle=True):
        inputs, targets = batch
        train_fn(inputs, targets)

or experiment with something a bit hacky (replace your code with this):

batch_size = 8
ix = 0
for step in range(num_training_steps):
    train_err = train_fn(x_train[ix*batch_size:ix*(batch_size + 1)], y_train[ix*batch_size:ix*(batch_size + 1)])

Note.. untested code without any error checking or array bounds checking...just some ideas to get you going.

Answer 2

I agree with guidot, it's likely a normal out-of-memory error.

Your input is a 384MB array followed by some dropout and dense layers of significant width.

Maybe decrease your first two Dense layers down from 800 to something else and give it another try. Perhaps decrease it to 600 and see if it runs longer but eventually gives the same error would give more evidence.