Exemplo n.º 1
0
    def get_output_for(self, input, **kwargs):
        num_feature_maps = input.shape[self.axis]
        num_pools = num_feature_maps // self.pool_size

        pool_shape = ()
        arange_shuffle_pattern = ()
        for k in range(self.axis):
            pool_shape += (input.shape[k],)
            arange_shuffle_pattern += ('x',)

        pool_shape += (num_pools, self.pool_size)
        arange_shuffle_pattern += ('x', 0)

        for k in range(self.axis + 1, input.ndim):
            pool_shape += (input.shape[k],)
            arange_shuffle_pattern += ('x',)

        input_reshaped = input.reshape(pool_shape)
        max_indices = T.argmax(input_reshaped, axis=self.axis + 1,
                               keepdims=True)

        arange = T.arange(self.pool_size).dimshuffle(*arange_shuffle_pattern)
        mask = T.eq(max_indices, arange).reshape(input.shape)

        return input * mask
Exemplo n.º 2
0
def main(model='mlp', num_epochs=500):
    # Load the dataset
    print("Loading data...")
    X_train, y_train, X_val, y_val, X_test, y_test = load_dataset()

    # Prepare Theano variables for inputs and targets
    input_var = T.tensor4('inputs', fixed_shape=(128,) + X_train.shape[1:])
    target_var = T.ivector('targets')

    # Create neural network model (depending on first command line parameter)
    print("Building model and compiling functions...")
    if model == 'mlp':
        network = build_mlp(input_var)
    elif model.startswith('custom_mlp:'):
        depth, width, drop_in, drop_hid = model.split(':', 1)[1].split(',')
        network = build_custom_mlp(input_var, int(depth), int(width),
                                   float(drop_in), float(drop_hid))
    elif model == 'cnn':
        network = build_cnn(input_var)
    else:
        print("Unrecognized model type %r." % model)
        return

    # Create a loss expression for training, i.e., a scalar objective we want
    # to minimize (for our multi-class problem, it is the cross-entropy loss):
    prediction = lasagne.layers.get_output(network)
    loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
    loss = T.mean(loss)
    # We could add some weight decay as well here, see lasagne.regularization.

    # Create update expressions for training, i.e., how to modify the
    # parameters at each training step. Here, we'll use Stochastic Gradient
    # Descent (SGD) with Nesterov momentum, but Lasagne offers plenty more.
    params = lasagne.layers.get_all_params(network, trainable=True)
    updates = lasagne.updates.nesterov_momentum(
            loss, params, learning_rate=0.01, momentum=0.9)

    # Create a loss expression for validation/testing. The crucial difference
    # here is that we do a deterministic forward pass through the network,
    # disabling dropout layers.
    test_prediction = lasagne.layers.get_output(network, deterministic=True)
    test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
                                                            target_var)
    test_loss = T.mean(test_loss)
    # As a bonus, also create an expression for the classification accuracy:
    test_acc = T.mean(T.cast(T.eq(
        T.cast(T.argmax(test_prediction, axis=1), 'int32'),
        T.cast(target_var, 'int32')
    ), theano.config.floatX))

    # Compile a function performing a training step on a mini-batch (by giving
    # the updates dictionary) and returning the corresponding training loss:
    train_fn = theano.function([input_var, target_var], loss, updates=updates)

    # Compile a second function computing the validation loss and accuracy:
    val_fn = theano.function([input_var, target_var], [test_loss, test_acc])

    # Finally, launch the training loop.
    print("Starting training...")
    # We iterate over epochs:
    for epoch in range(num_epochs):
        # In each epoch, we do a full pass over the training data:
        train_err = 0
        train_batches = 0
        start_time = time.time()
        for batch in iterate_minibatches(X_train, y_train, 128, shuffle=True):
            inputs, targets = batch
            train_err += train_fn(inputs, targets)
            train_batches += 1

        # And a full pass over the validation data:
        val_err = 0
        val_acc = 0
        val_batches = 0
        for batch in iterate_minibatches(X_val, y_val, 128, shuffle=False):
            inputs, targets = batch
            err, acc = val_fn(inputs, targets)
            val_err += err
            val_acc += acc
            val_batches += 1

        # Then we print the results for this epoch:
        print("Epoch {} of {} took {:.3f}s".format(
            epoch + 1, num_epochs, time.time() - start_time))
        print("  training loss:\t\t{:.6f}".format(train_err / train_batches))
        print("  validation loss:\t\t{:.6f}".format(val_err / val_batches))
        print("  validation accuracy:\t\t{:.2f} %".format(
            val_acc / val_batches * 100))

    # After training, we compute and print the test error:
    test_err = 0
    test_acc = 0
    test_batches = 0
    for batch in iterate_minibatches(X_test, y_test, 128, shuffle=False):
        inputs, targets = batch
        err, acc = val_fn(inputs, targets)
        test_err += err
        test_acc += acc
        test_batches += 1
    print("Final results:")
    print("  test loss:\t\t\t{:.6f}".format(test_err / test_batches))
    print("  test accuracy:\t\t{:.2f} %".format(
        test_acc / test_batches * 100))