import tensorflow as tf
import numpy as np
import math
import timeit
import matplotlib.pyplot as plt
%matplotlib inline

from cs231n.data_utils import load_CIFAR10

def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):
    """
    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare
    it for the two-layer neural net classifier. These are the same steps as
    we used for the SVM, but condensed to a single function.  
    """
    # Load the raw CIFAR-10 data
    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'
    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)

    # Subsample the data
    mask = range(num_training, num_training + num_validation)
    X_val = X_train[mask]
    y_val = y_train[mask]
    mask = range(num_training)
    X_train = X_train[mask]
    y_train = y_train[mask]
    mask = range(num_test)
    X_test = X_test[mask]
    y_test = y_test[mask]

    # Normalize the data: subtract the mean image
    mean_image = np.mean(X_train, axis=0)
    X_train -= mean_image
    X_val -= mean_image
    X_test -= mean_image

    return X_train, y_train, X_val, y_val, X_test, y_test


# Invoke the above function to get our data.
X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()
print('Train data shape: ', X_train.shape)
print('Train labels shape: ', y_train.shape)
print('Validation data shape: ', X_val.shape)
print('Validation labels shape: ', y_val.shape)
print('Test data shape: ', X_test.shape)
print('Test labels shape: ', y_test.shape)

"""
Explanation: What's this TensorFlow business?
You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.
For the last part of this assignment, though, we're going to leave behind your beautiful codebase and instead migrate to one of two popular deep learning frameworks: in this instance, TensorFlow (or PyTorch, if you switch over to that notebook)
What is it?
TensorFlow is a system for executing computational graphs over Tensor objects, with native support for performing backpropogation for its Variables. In it, we work with Tensors which are n-dimensional arrays analogous to the numpy ndarray.
Why?

Our code will now run on GPUs! Much faster training. Writing your own modules to run on GPUs is beyond the scope of this class, unfortunately.
We want you to be ready to use one of these frameworks for your project so you can experiment more efficiently than if you were writing every feature you want to use by hand. 
We want you to stand on the shoulders of giants! TensorFlow and PyTorch are both excellent frameworks that will make your lives a lot easier, and now that you understand their guts, you are free to use them :) 
We want you to be exposed to the sort of deep learning code you might run into in academia or industry. 

How will I learn TensorFlow?
TensorFlow has many excellent tutorials available, including those from Google themselves.
Otherwise, this notebook will walk you through much of what you need to do to train models in TensorFlow. See the end of the notebook for some links to helpful tutorials if you want to learn more or need further clarification on topics that aren't fully explained here.
Load Datasets
End of explanation
"""


# clear old variables
tf.reset_default_graph()

# setup input (e.g. the data that changes every batch)
# The first dim is None, and gets sets automatically based on batch size fed in
X = tf.placeholder(tf.float32, [None, 32, 32, 3]) # NxHxWxC == NHWC format, not shape, not type, maybe structure
y = tf.placeholder(tf.int64, [None])
is_training = tf.placeholder(tf.bool)

def simple_model(X,y):
    # define our weights (e.g. init_two_layer_convnet)
    
    # setup variables
    Wconv1 = tf.get_variable("Wconv1", shape=[7, 7, 3, 32])
    bconv1 = tf.get_variable("bconv1", shape=[32])
    W1 = tf.get_variable("W1", shape=[5408, 10])
    b1 = tf.get_variable("b1", shape=[10])

    # define our graph (e.g. two_layer_convnet)
    a1 = tf.nn.conv2d(X, Wconv1, strides=[1,2,2,1], padding='VALID') + bconv1
    h1 = tf.nn.relu(a1)
    h1_flat = tf.reshape(h1,[-1,5408])
    y_out = tf.matmul(h1_flat,W1) + b1
    return y_out

y_out = simple_model(X,y)

# define our loss
total_loss = tf.losses.hinge_loss(tf.one_hot(y,10),logits=y_out)
mean_loss = tf.reduce_mean(total_loss)

# define our optimizer
optimizer = tf.train.AdamOptimizer(5e-4) # select optimizer and set learning rate
train_step = optimizer.minimize(mean_loss)

# # NHWC format for xy data, input and output data
# xN, xH, xW, xC = None, 32, 32, 3
# # kN, kH, kW, kC = number conv kernel, conv kernel height, conv kernel width, conv kernel channel
# #     Wconv1 = tf.get_variable("Wconv1", shape=[7, 7, 3, 32]) # kHxkWxkCxkN, HWCN format for conv kernel/ conv neuron
# kH, kW, kC, kN = 7, 7, 3, 32
# # conv stride, conv pad
# stride, pad = [1, 2, 2, 1], 'VALID'
# # stride = [sN, sH, sW, sC] probablly the same as NHWC format of the input data x or X
# # NHWC is the default tensor data format
# #  striding the conv kernel/ filter on the input tensor data padded or not padded
# # usually stride[0] == stride[3]
# #  conv stride we should notice that this is a 2d conv meaning the kernel moving in height and width direction
# # for padding we should notice that we want the conv output shape be shrining/ reducing the dimensionality
# # validating our convolution output size for dimension reduction, shrinking the input
# # or the same shape/ size for conv output compared to the conv input
# # with valid, we should expect PAD=0, therefore the conv output shape/ size can be calculated as below
# yH = ((xH - kH)/ stride) + 1 # one conv direction in height
# yW = ((xW - kW)/stride) + 1 # one conv direction in width
# yC = kN # number of conv neurons or kernels or filters
# yN = xN # number of input dimensions/ features
# # tf.get_variable(caching_device=, collections=, custom_getter=, dtype=, initializer=, name=, 
# #                          partitioner=, regularizer=, shape=, trainable=, use_resource=, validate_shape=)
# # Wconv = tf.get_variable(name='Wconv', dtype=tf.float64, shape=[kH, kW, kC, kN], trainable=True)
# # bconv = tf.get_variable(dtype=tf.float64, name='bconv', shape=[kN], trainable=True)
# # After convolution, we should flatten the yShape to feed it into the fc layer or fully-connected layer
# # If we don't apply the pooling to the conv output, we should flatten the tensor output of conv to feed it into the
# # FC layer parameters for W and b
# # xN, xD, xK = minibatch size/ number of input sampels, number of FC input dimensions, number of FC neurons
# # xN, xD, xK = 
# # W = tf.get_variable(dtype=, name=, shape=[yH*yW*yC, K], trainable=True)
# # xD = yH
# # NHWC
# yH*yW*yC = xD


# # NHWC format for x, k, y
# xN, xH, xW, xC = None, 32, 32, 3
# # kH, kW, kC, kN = 7, 7, 3, 32
# kH, kW, kC, kN = 8, 8, 3, 32
# # yN, yH, yW, yC = None, 13, 13, 32
# # stride, padding = [1, 2, 2, 1], 'VALID'
# sN, sH, sW, sC = 1, 2, 2, 1
# # p = 0
# ###     stride = [sN, sH, sW, sC] for NHWC format
# yH = ((xH - kH)/ sH) + 1 # one conv direction in height
# yW = ((xW - kW)/ sW) + 1 # one conv direction in width
# yC = kN
# #     yC = kN # number of conv neurons or kernels or filters
# #     yN = xN # number of input dimensions/ features
# #     yH*yW*yC = xD
# # C = 10 # number of output classes
# yH, yW, yC, int(yH* yW* yC)

# # pooling: is also a type of convolution inspired by complex cells, a filter but not filter bank
# # designed/ defined for more robustness/ onvariancy or equivariancy??
# pH, pW, pC, pN = 2, 2, 1, 1
# sN, sH, sW, sC = 1, 1, 1, 1
# # pad = 0


# clear old variables
tf.reset_default_graph()

# setup input (e.g. the data that changes every batch)
# The first dim is None, and gets sets automatically based on batch size fed in
X = tf.placeholder(tf.float32, [None, 32, 32, 3]) # NxHxWxC == NHWC format, not shape, not type, maybe structure
y = tf.placeholder(tf.int64, [None])
is_training = tf.placeholder(tf.bool)

def simple_model(X,y):
    # define our weights (e.g. init_two_layer_convnet)
        
    # setup variables
    Wconv1 = tf.get_variable("Wconv1", shape=[7, 7, 3, 32])
    bconv1 = tf.get_variable("bconv1", shape=[32])
    W1 = tf.get_variable("W1", shape=[13*13*32, 10]) # 5408 = 13 * 13 * 32
    b1 = tf.get_variable("b1", shape=[10])

    # define our graph (e.g. two_layer_convnet)
    a1 = tf.nn.conv2d(X, Wconv1, strides=[1,2,2,1], padding='VALID') + bconv1
    h1 = tf.nn.relu(a1)
    h1_flat = tf.reshape(name='h1_flat', shape=[-1, 13*13*13], tensor=h1) # 5408 = 13 * 13 * 32
    y_out = tf.matmul(h1_flat,W1) + b1
    return y_out

y_out = simple_model(X,y)

# define our loss
total_loss = tf.losses.hinge_loss(tf.one_hot(y,10),logits=y_out)
mean_loss = tf.reduce_mean(total_loss)

# define our optimizer
optimizer = tf.train.AdamOptimizer(5e-4) # select optimizer and set learning rate
train_step = optimizer.minimize(mean_loss)

"""
Explanation: Example Model
Some useful utilities
. Remember that our image data is initially N x H x W x C, where:
* N is the number of datapoints
* H is the height of each image in pixels
* W is the height of each image in pixels
* C is the number of channels (usually 3: R, G, B)
This is the right way to represent the data when we are doing something like a 2D convolution, which needs spatial understanding of where the pixels are relative to each other. When we input image data into fully connected affine layers, however, we want each data example to be represented by a single vector -- it's no longer useful to segregate the different channels, rows, and columns of the data.
The example model itself
The first step to training your own model is defining its architecture.
Here's an example of a convolutional neural network defined in TensorFlow -- try to understand what each line is doing, remembering that each layer is composed upon the previous layer. We haven't trained anything yet - that'll come next - for now, we want you to understand how everything gets set up. 
In that example, you see 2D convolutional layers (Conv2d), ReLU activations, and fully-connected layers (Linear). You also see the Hinge loss function, and the Adam optimizer being used. 
Make sure you understand why the parameters of the Linear layer are 5408 and 10.
TensorFlow Details
In TensorFlow, much like in our previous notebooks, we'll first specifically initialize our variables, and then our network model.
End of explanation
"""


def run_model(session, predict, loss_val, Xd, yd,
              epochs=1, batch_size=64, print_every=100,
              training=None, plot_losses=False):
    # have tensorflow compute accuracy
    correct_prediction = tf.equal(tf.argmax(predict,1), y)
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    
    # shuffle indicies
    train_indicies = np.arange(Xd.shape[0])
    np.random.shuffle(train_indicies)

    training_now = training is not None
    
    # setting up variables we want to compute (and optimizing)
    # if we have a training function, add that to things we compute
    variables = [mean_loss,correct_prediction,accuracy]
    if training_now:
        variables[-1] = training
    
    # counter 
    iter_cnt = 0
    for e in range(epochs):
        # keep track of losses and accuracy
        correct = 0
        losses = []
        # make sure we iterate over the dataset once
        for i in range(int(math.ceil(Xd.shape[0]/batch_size))):
            # generate indicies for the batch
            start_idx = (i*batch_size)%Xd.shape[0]
            idx = train_indicies[start_idx:start_idx+batch_size]
            
            # create a feed dictionary for this batch
            feed_dict = {X: Xd[idx,:],
                         y: yd[idx],
                         is_training: training_now }
            # get batch size
            actual_batch_size = yd[idx].shape[0]
            
            # have tensorflow compute loss and correct predictions
            # and (if given) perform a training step
            loss, corr, _ = session.run(variables,feed_dict=feed_dict)
            
            # aggregate performance stats
            losses.append(loss*actual_batch_size)
            correct += np.sum(corr)
            
            # print every now and then
            if training_now and (iter_cnt % print_every) == 0:
                print("Iteration {0}: with minibatch training loss = {1:.3g} and accuracy of {2:.2g}"\
                      .format(iter_cnt,loss,np.sum(corr)/actual_batch_size))
            iter_cnt += 1
        total_correct = correct/Xd.shape[0]
        total_loss = np.sum(losses)/Xd.shape[0]
        print("Epoch {2}, Overall loss = {0:.3g} and accuracy of {1:.3g}"\
              .format(total_loss,total_correct,e+1))
        if plot_losses:
            plt.plot(losses)
            plt.grid(True)
            plt.title('Epoch {} Loss'.format(e+1))
            plt.xlabel('minibatch number')
            plt.ylabel('minibatch loss')
            plt.show()
    return total_loss,total_correct

with tf.Session() as sess:
    with tf.device("/cpu:0"): #"/cpu:0" or "/gpu:0" 
        sess.run(tf.global_variables_initializer())
        print('Training')
        run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step,True)
        print('Validation')
        run_model(sess,y_out,mean_loss,X_val,y_val,1,64)

"""
Explanation: TensorFlow supports many other layer types, loss functions, and optimizers - you will experiment with these next. Here's the official API documentation for these (if any of the parameters used above were unclear, this resource will also be helpful). 

Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn
Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers
BatchNorm: https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization

Training the model on one epoch
While we have defined a graph of operations above, in order to execute TensorFlow Graphs, by feeding them input data and computing the results, we first need to create a tf.Session object. A session encapsulates the control and state of the TensorFlow runtime. For more information, see the TensorFlow Getting started guide.
Optionally we can also specify a device context such as /cpu:0 or /gpu:0. For documentation on this behavior see this TensorFlow guide
You should see a validation loss of around 0.4 to 0.6 and an accuracy of 0.30 to 0.35 below
End of explanation
"""


# clear old variables
tf.reset_default_graph()

# define our input (e.g. the data that changes every batch)
# The first dim is None, and gets sets automatically based on batch size fed in
X = tf.placeholder(tf.float32, [None, 32, 32, 3])
y = tf.placeholder(tf.int64, [None])
is_training = tf.placeholder(tf.bool)

# define model
def complex_model(X,y,is_training):
    
    # 7x7 conv layer, 32 filter, stride 1, pad=?, input data size?, output size?, num channels in conv== input channel
    # in.shape= NxCxHxW,
    # conv kernel/ filter/ receptive field/ synaptic field = 32xCx7x7
    # stride = 1, pad = 'SAME' or 'VALID' or 'FULL'??
    # out.shape equation Nx32xOHxOW
    # OH = (H + 2*pad - kH)/ stride + 1
    # OW = (W + 2*pad - kW)/ stride + 1
    Wconv = tf.get_variable(dtype=, name=, shape=, trainable=is_training)
    tf.nn.conv2d(data_format=,filter=,filter_sizes=,input=,input_sizes=,name=,out_backprop=,output_shape=,padding=,strides=,use_cudnn_on_gpu=,value=)
    
    # non-linearity/ activation function/ activation layer/ rectified linear unit (ReLU)
    tf.nn.relu(biases=, features=, name=, weights=, x=)
    
    # Spatial batch normalization with trainable/learnable parameters with scale and centering
    # mean, scale/ std/ variance, gamma, beta, running average, running variance, momentum
    tf.nn.batch_normalization(mean=, name=, offset=, scale=, variance=, variance_epsilon=, x=)
    
    # 2x2 max pooling layer with stride 2
    # average pooling or max pooling
    # pooling is a convolution as well
    # max pooling is a non-linear convolutional operation
    # average pooling is a linear convolutional operation
    # There is a big difference between the first convoltion and pooling which is also a convolution.
    # simple cell and complex cell idea is the inspiration behind these twp convolutional approach
    # complex cell is the inpirational idea/ idea/ inspiration behind pooling
    # Since pooling is also a convolutional approach:
    # receptive field/ synaptic field/ number of synapses/ conv input/ kernel size: kN, kC, kH, kW
    # conv size: kN x kC x kH x kW, conv stride, conv pad, conv out, conv input
    # This convlution operation mimiking complex cell receptive field/ functionality very loosely/ vaguely is pooling.
    # pool input, pool kernel size, pool stride, pool pad, pool output
    # Pooling is intended to bring robustness/ reliability to the features and downsample the input/ less input samples
    tf.nn.pool(data_format=, dilation_rate=, input=, name=, padding=, pooling_type=, strides=, window_shape=)
    
    # After conv, bn, pool, we have a fully connected layer/ dense which is the same as affine layer.
    # AT this point, we should flatten the tensor output of the pooling layer;
    # we should feed into a fully connected layer with many neurons/ hidden units
    # This is an affine layer/ fully connected layer with 1024 units/
    out_pool.reshape(N, -1)
    
    # Create a variable W or weights or synaptic weights or pre-synaptic weights to post synaptic weights and number of neurons
    # Create biases as well the same as number of neurons in the fully connected layer
    
    # number of neurons to number of output classes which is another fully connected layer
    # create the weights and biases
    pass

y_out = complex_model(X,y,is_training)

"""
Explanation: Training a specific model
In this section, we're going to specify a model for you to construct. The goal here isn't to get good performance (that'll be next), but instead to get comfortable with understanding the TensorFlow documentation and configuring your own model. 
Using the code provided above as guidance, and using the following TensorFlow documentation, specify a model with the following architecture:

7x7 Convolutional Layer with 32 filters and stride of 1
ReLU Activation Layer
Spatial Batch Normalization Layer (trainable parameters, with scale and centering)
2x2 Max Pooling layer with a stride of 2
Affine layer with 1024 output units
ReLU Activation Layer
Affine layer from 1024 input units to 10 outputs
End of explanation
"""


# Now we're going to feed a random batch into the model 
# and make sure the output is the right size
x = np.random.randn(64, 32, 32,3)
with tf.Session() as sess:
    with tf.device("/cpu:0"): #"/cpu:0" or "/gpu:0"
        tf.global_variables_initializer().run()

        ans = sess.run(y_out,feed_dict={X:x,is_training:True})
        %timeit sess.run(y_out,feed_dict={X:x,is_training:True})
        print(ans.shape)
        print(np.array_equal(ans.shape, np.array([64, 10])))

"""
Explanation: To make sure you're doing the right thing, use the following tool to check the dimensionality of your output (it should be 64 x 10, since our batches have size 64 and the output of the final affine layer should be 10, corresponding to our 10 classes):
End of explanation
"""


try:
    with tf.Session() as sess:
        with tf.device("/gpu:0") as dev: #"/cpu:0" or "/gpu:0"
            tf.global_variables_initializer().run()

            ans = sess.run(y_out,feed_dict={X:x,is_training:True})
            %timeit sess.run(y_out,feed_dict={X:x,is_training:True})
except tf.errors.InvalidArgumentError:
    print("no gpu found, please use Google Cloud if you want GPU acceleration")    
    # rebuild the graph
    # trying to start a GPU throws an exception 
    # and also trashes the original graph
    tf.reset_default_graph()
    X = tf.placeholder(tf.float32, [None, 32, 32, 3])
    y = tf.placeholder(tf.int64, [None])
    is_training = tf.placeholder(tf.bool)
    y_out = complex_model(X,y,is_training)

"""
Explanation: You should see the following from the run above 
(64, 10)
True
GPU!
Now, we're going to try and start the model under the GPU device, the rest of the code stays unchanged and all our variables and operations will be computed using accelerated code paths. However, if there is no GPU, we get a Python exception and have to rebuild our graph. On a dual-core CPU, you might see around 50-80ms/batch running the above, while the Google Cloud GPUs (run below) should be around 2-5ms/batch.
End of explanation
"""


# Inputs
#     y_out: is what your model computes
#     y: is your TensorFlow variable with label information
# Outputs
#    mean_loss: a TensorFlow variable (scalar) with numerical loss
#    optimizer: a TensorFlow optimizer
# This should be ~3 lines of code!
mean_loss = None
optimizer = None
pass


# batch normalization in tensorflow requires this extra dependency
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
    train_step = optimizer.minimize(mean_loss)

"""
Explanation: You should observe that even a simple forward pass like this is significantly faster on the GPU. So for the rest of the assignment (and when you go train your models in assignment 3 and your project!), you should use GPU devices. However, with TensorFlow, the default device is a GPU if one is available, and a CPU otherwise, so we can skip the device specification from now on.
Train the model.
Now that you've seen how to define a model and do a single forward pass of some data through it, let's  walk through how you'd actually train one whole epoch over your training data (using the complex_model you created provided above).
Make sure you understand how each TensorFlow function used below corresponds to what you implemented in your custom neural network implementation.
First, set up an RMSprop optimizer (using a 1e-3 learning rate) and a cross-entropy loss function. See the TensorFlow documentation for more information
* Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn
* Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers
End of explanation
"""


sess = tf.Session()

sess.run(tf.global_variables_initializer())
print('Training')
run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step)

"""
Explanation: Train the model
Below we'll create a session and train the model over one epoch. You should see a loss of 1.4 to 2.0 and an accuracy of 0.4 to 0.5. There will be some variation due to random seeds and differences in initialization
End of explanation
"""


print('Validation')
run_model(sess,y_out,mean_loss,X_val,y_val,1,64)

"""
Explanation: Check the accuracy of the model.
Let's see the train and test code in action -- feel free to use these methods when evaluating the models you develop below. You should see a loss of 1.3 to 2.0 with an accuracy of 0.45 to 0.55.
End of explanation
"""


# Feel free to play with this cell

def my_model(X,y,is_training):
    pass

tf.reset_default_graph()

X = tf.placeholder(tf.float32, [None, 32, 32, 3])
y = tf.placeholder(tf.int64, [None])
is_training = tf.placeholder(tf.bool)

y_out = my_model(X,y,is_training)
mean_loss = None
optimizer = None


pass

# batch normalization in tensorflow requires this extra dependency
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
    train_step = optimizer.minimize(mean_loss)

# Feel free to play with this cell
# This default code creates a session
# and trains your model for 10 epochs
# then prints the validation set accuracy
sess = tf.Session()

sess.run(tf.global_variables_initializer())
print('Training')
run_model(sess,y_out,mean_loss,X_train,y_train,10,64,100,train_step,True)
print('Validation')
run_model(sess,y_out,mean_loss,X_val,y_val,1,64)

# Test your model here, and make sure 
# the output of this cell is the accuracy
# of your best model on the training and val sets
# We're looking for >= 70% accuracy on Validation
print('Training')
run_model(sess,y_out,mean_loss,X_train,y_train,1,64)
print('Validation')
run_model(sess,y_out,mean_loss,X_val,y_val,1,64)

"""
Explanation: Train a great model on CIFAR-10!
Now it's your job to experiment with architectures, hyperparameters, loss functions, and optimizers to train a model that achieves  >= 70% accuracy on the validation set of CIFAR-10. You can use the run_model function from above.
Things you should try:

Filter size: Above we used 7x7; this makes pretty pictures but smaller filters may be more efficient
Number of filters: Above we used 32 filters. Do more or fewer do better?
Pooling vs Strided Convolution: Do you use max pooling or just stride convolutions?
Batch normalization: Try adding spatial batch normalization after convolution layers and vanilla batch normalization after affine layers. Do your networks train faster?
Network architecture: The network above has two layers of trainable parameters. Can you do better with a deep network? Good architectures to try include:
[conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]
[conv-relu-conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]
[batchnorm-relu-conv]xN -> [affine]xM -> [softmax or SVM]


Use TensorFlow Scope: Use TensorFlow scope and/or tf.layers to make it easier to write deeper networks. See this tutorial for how to use tf.layers. 
Use Learning Rate Decay: As the notes point out, decaying the learning rate might help the model converge. Feel free to decay every epoch, when loss doesn't change over an entire epoch, or any other heuristic you find appropriate. See the Tensorflow documentation for learning rate decay.
Global Average Pooling: Instead of flattening and then having multiple affine layers, perform convolutions until your image gets small (7x7 or so) and then perform an average pooling operation to get to a 1x1 image picture (1, 1 , Filter#), which is then reshaped into a (Filter#) vector. This is used in Google's Inception Network (See Table 1 for their architecture).
Regularization: Add l2 weight regularization, or perhaps use Dropout as in the TensorFlow MNIST tutorial

Tips for training
For each network architecture that you try, you should tune the learning rate and regularization strength. When doing this there are a couple important things to keep in mind:

If the parameters are working well, you should see improvement within a few hundred iterations
Remember the coarse-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.
Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.
You should use the validation set for hyperparameter search, and we'll save the test set for evaluating your architecture on the best parameters as selected by the validation set.

Going above and beyond
If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are not required to implement any of these; however they would be good things to try for extra credit.

Alternative update steps: For the assignment we implemented SGD+momentum, RMSprop, and Adam; you could try alternatives like AdaGrad or AdaDelta.
Alternative activation functions such as leaky ReLU, parametric ReLU, ELU, or MaxOut.
Model ensembles
Data augmentation
New Architectures
ResNets where the input from the previous layer is added to the output.
DenseNets where inputs into previous layers are concatenated together.
This blog has an in-depth overview

If you do decide to implement something extra, clearly describe it in the "Extra Credit Description" cell below.
What we expect
At the very least, you should be able to train a ConvNet that gets at >= 70% accuracy on the validation set. This is just a lower bound - if you are careful it should be possible to get accuracies much higher than that! Extra credit points will be awarded for particularly high-scoring models or unique approaches.
You should use the space below to experiment and train your network. The final cell in this notebook should contain the training and validation set accuracies for your final trained network.
Have fun and happy training!
End of explanation
"""


print('Test')
run_model(sess,y_out,mean_loss,X_test,y_test,1,64)

"""
Explanation: Describe what you did here
In this cell you should also write an explanation of what you did, any additional features that you implemented, and any visualizations or graphs that you make in the process of training and evaluating your network
Tell us here
Test Set - Do this only once
Now that we've gotten a result that we're happy with, we test our final model on the test set. This would be the score we would achieve on a competition. Think about how this compares to your validation set accuracy.
End of explanation
"""


