Learning deep learning (project 2, image classification)

In this class project, I built a network to classify images in the CIFAR-10 dataset. This dataset is freely available.

The dataset contains 60K color images (32×32 pixel) in 10 classes, with 6K images per class.

Here are the classes in the dataset, as well as 10 random images from each:

airplane
automobile
bird
cat
deer
dog
frog
horse
ship
truck

You can imagine it’s not possible to write down all rules to classify them, so we have to write a program which can learn.

The neural network I created contains 2 hidden layers. The first one is a convolutional layer with max pooling. Then drop out 70% of the connections. The second layer is a fully connected layer with 384 neurons.

def conv_net(x, keep_prob):
    """
    Create a convolutional neural network model
    : x: Placeholder tensor that holds image data.
    : keep_prob: Placeholder tensor that hold dropout keep probability.
    : return: Tensor that represents logits
    """
    # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers
    #    Play around with different number of outputs, kernel size and stride
    # Function Definition from Above:
    #    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)
    model = conv2d_maxpool(x, conv_num_outputs=18, conv_ksize=(4,4), conv_strides=(1,1), pool_ksize=(8,8), pool_strides=(1,1))
    model = tf.nn.dropout(model, keep_prob)

    # TODO: Apply a Flatten Layer
    # Function Definition from Above:
    #   flatten(x_tensor)
    model = flatten(model)

    # TODO: Apply 1, 2, or 3 Fully Connected Layers
    #    Play around with different number of outputs
    # Function Definition from Above:
    #   fully_conn(x_tensor, num_outputs)
    model = fully_conn(model,384)

    model = tf.nn.dropout(model, keep_prob)

    # TODO: Apply an Output Layer
    #    Set this to the number of classes
    # Function Definition from Above:
    #   output(x_tensor, num_outputs)
    model = output(model,10)

    # TODO: return output
    return model

Then I trained this network using Amazon AWS g2.2xlarge instance. This instance has GPU which is much faster for deep learning (than CPU). I did a simple experiment and find GPU is at least 3 times faster than CPU:

if all layers in gpu: 14 seconds to run 4 epochs,
if conv layer in cpu, other gpu, 36 seconds to run 4 epochs

This is apparently a very crude comparison but GPU is definitely much faster than CPU (at least the ones in AWS g2.2xlarge, cost: $0.65/hour)

Eventually I got ~70% accuracy on the test data, much better than random guess (10%). The time to train the model is ~30 minutes.

You can find my entire code at:
https://www.alivelearn.net/deeplearning/dlnd_image_classification_submission2.html

---

Receive email notification via email

---

Don't want to miss new papers in your field? Check out Stork we developed:

专注医学生物类的论文润色 联系他们请用优惠码 STORK4，他们会给你折扣。

---