# TensorFlow Note
## Tutorial
### Image Classification With TensorFlow 2.0 ( Without Keras )
- https://becominghuman.ai/image-classification-with-tensorflow-2-0-without-keras-e6534adddab2

### import the data ###
import tensorflow_datasets as tfds

dataset_name = 'horses_or_humans'

dataset = tfds.load(name=dataset_name, split=tfds.Split.TRAIN)
dataset = dataset.shuffle(1024).batch(batch_size)

### define the model and related ops ###
leaky_relu_alpha = 0.2
dropout_rate = 0.5

def conv2d(inputs, filter, stride_size):
    out = tf.nn.conv2d(inputs, filters, strides=[1, stride_size, stride_size, 1], padding=padding)
    return tf.nn.leaky_relu(out, alpha=leaky_relu_alpha)
    
def maxpool(inputs, pool_size, stride_size):
    return tf.nn.max_pool2d(inputs, ksize=[1, pool_size, pool_size, 1], padding='VALID', strides = [1, stride_size, stride_size, 1])
    
def dense(inputs, weights);
    x = tf.nn.leaky_relu(tf.matmul(inputs, weights), alpha=leaky_relu_alpha)
    return tf.nn.dropout(x, rate=dropout_rate)
    
initializer = tf.initalizers.glorot_uniform()
def get_weight(shape, name):
    return tf.Variable(initializer(shape), name=name, trainable=True, dtype=tf.float32)
    
shapes = [
    [ 3 , 3 , 3 , 16 ] , 
    [ 3 , 3 , 16 , 16 ] , 
    [ 3 , 3 , 16 , 32 ] , 
    [ 3 , 3 , 32 , 32 ] ,
    [ 3 , 3 , 32 , 64 ] , 
    [ 3 , 3 , 64 , 64 ] ,
    [ 3 , 3 , 64 , 128 ] , 
    [ 3 , 3 , 128 , 128 ] ,
    [ 3 , 3 , 128 , 256 ] , 
    [ 3 , 3 , 256 , 256 ] ,
    [ 3 , 3 , 256 , 512 ] , 
    [ 3 , 3 , 512 , 512 ] ,
    [ 8192 , 3600 ] , 
    [ 3600 , 2400 ] ,
    [ 2400 , 1600 ] , 
    [ 1600 , 800 ] ,
    [ 800 , 64 ] ,
    [ 64 , output_classes ] ,
]

weights = []
for i in range(len(shapes)):
    weights.append(get_weight(shapes[i], 'weight{}'.format(i)))
    
def model( x ) :
    x = tf.cast( x , dtype=tf.float32 )
    c1 = conv2d( x , weights[ 0 ] , stride_size=1 ) 
    c1 = conv2d( c1 , weights[ 1 ] , stride_size=1 ) 
    p1 = maxpool( c1 , pool_size=2 , stride_size=2 )
    
    c2 = conv2d( p1 , weights[ 2 ] , stride_size=1 )
    c2 = conv2d( c2 , weights[ 3 ] , stride_size=1 ) 
    p2 = maxpool( c2 , pool_size=2 , stride_size=2 )
    
    c3 = conv2d( p2 , weights[ 4 ] , stride_size=1 ) 
    c3 = conv2d( c3 , weights[ 5 ] , stride_size=1 ) 
    p3 = maxpool( c3 , pool_size=2 , stride_size=2 )
    
    c4 = conv2d( p3 , weights[ 6 ] , stride_size=1 )
    c4 = conv2d( c4 , weights[ 7 ] , stride_size=1 )
    p4 = maxpool( c4 , pool_size=2 , stride_size=2 )

    c5 = conv2d( p4 , weights[ 8 ] , stride_size=1 )
    c5 = conv2d( c5 , weights[ 9 ] , stride_size=1 )
    p5 = maxpool( c5 , pool_size=2 , stride_size=2 )

    c6 = conv2d( p5 , weights[ 10 ] , stride_size=1 )
    c6 = conv2d( c6 , weights[ 11 ] , stride_size=1 )
    p6 = maxpool( c6 , pool_size=2 , stride_size=2 )

    flatten = tf.reshape( p6 , shape=( tf.shape( p6 )[0] , -1 ))

    d1 = dense( flatten , weights[ 12 ] )
    d2 = dense( d1 , weights[ 13 ] )
    d3 = dense( d2 , weights[ 14 ] )
    d4 = dense( d3 , weights[ 15 ] )
    d5 = dense( d4 , weights[ 16 ] )
    logits = tf.matmul( d5 , weights[ 17 ] )

    return tf.nn.softmax( logits )
    
def loss(pred, target):
    return tf.losses.categorical_crossentropy(target, pred)
    
optimizer = tf.optimizers.Adam(learning_rate)

def train_step(model, inputs, outputs):
    with tf.GradientTape() as tape:
        current_loss = loss(model(inputs), outputs)
    grads = tape.gradient(current_loss, weights)
    optimizer.apply_gradients(zip(grads, weights))
    print(tf.reduce_mean(current_loss))
    
num_epochs = 256

for e in range(num_epochs):
    for features in dataset:
        image, label = features['image'], feature['label']
        train_step(model, image, tf.one_hot(label, depth=3))