eileen-code4fun

import tensorflow as tf

def normalize(image, label):
  return tf.cast(image, dtype=tf.float32) / 255.0, label

train = ds['train'].map(normalize).batch(32)
test = ds['test'].map(normalize).batch(32)

encoding_model = tf.keras.Model(inputs=model.input, outputs=model.get_layer('flatten').output)

eileen-code4fun

import tensorflow_data_validation as tfdv
from tensorflow_metadata.proto.v0 import anomalies_pb2

stats = tfdv.load_stats_binary(GCS_PATH_FOR_STATS_PB)
tfdv.visualize_statistics(stats)
# Display output in screenshot later.

schema = tfdv.load_schema_text(GCS_PATH_FOR_SCHEMA_PBTXT)
tfdv.display_schema(schema)
# Display output in screenshot later.

eileen-code4fun

import dgl
import tensorflow as tf

dataset = dgl.data.CoraGraphDataset()

# A DGL dataset may contain multiple graphs.
# In the case of Cora, there is only one graph.
g = dataset[0]

# g.ndata is a dictionary of nodes related data.

eileen-code4fun

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Input(shape=(1433,)))
model.add(tf.keras.layers.Dense(32, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(7))

model.compile(
    optimizer='adam',
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=['accuracy'])

eileen-code4fun

from dgl.nn.tensorflow import GraphConv

class GCN(tf.keras.Model):
    def __init__(self, g, feat_dim, hidden_dim, class_num):
        super(GCN, self).__init__()
        self.g = g
        self.h1 = GraphConv(feat_dim, hidden_dim, activation=tf.nn.relu, allow_zero_in_degree=True)
        self.dropout = tf.keras.layers.Dropout(0.5)
        self.h2 = GraphConv(hidden_dim, class_num), allow_zero_in_degree=True
    

eileen-code4fun

for epoch in range(20):
    with tf.GradientTape() as tape:
        logits = g_model(g.ndata['feat'])
        # Loss is only calculated on the training set.
        loss = loss_fcn(g.ndata['label'][g.ndata['train_mask']], logits[g.ndata['train_mask']])
        grads = tape.gradient(loss, g_model.trainable_weights)
        opt.apply_gradients(zip(grads, g_model.trainable_weights))

eileen-code4fun

import dgl
import tensorflow as tf

def eval(model, g, node_ids_to_eval):
    logits = model(g, g.ndata['feat'], training=False)
    logits = tf.gather(logits, node_ids_to_eval)
    labels = tf.gather(g.ndata['label'], node_ids_to_eval)
    indices = tf.math.argmax(logits, axis=1)
    acc = tf.reduce_mean(tf.cast(indices == labels, dtype=tf.float32))
    return acc.numpy().item()

eileen-code4fun

from dgl.nn.tensorflow import GraphConv

class GCN(tf.keras.Model):
    def __init__(self, feat_dim, hidden_dim, class_num):
        super(GCN, self).__init__()
        self.h1 = GraphConv(feat_dim, hidden_dim, activation=tf.nn.relu, allow_zero_in_degree=True)
        self.dropout = tf.keras.layers.Dropout(0.5)
        self.h2 = GraphConv(hidden_dim, class_num, allow_zero_in_degree=True)
    
    def call(self, g, features):

eileen-code4fun

from dgl.nn.tensorflow import SAGEConv

class SAGE(tf.keras.Model):
    def __init__(self, feat_dim, hidden_dim, class_num):
        super(SAGE, self).__init__()
        self.h1 = SAGEConv(in_feats=feat_dim, out_feats=hidden_dim, aggregator_type='pool', feat_drop=0.5, activation=tf.nn.relu)
        self.h2 = SAGEConv(in_feats=feat_dim, out_feats=class_num, aggregator_type='pool')
    
    def call(self, g, features):
        h = features

eileen-code4fun

from dgl.nn.tensorflow import GINConv

class GIN(tf.keras.Model):
    def mlp(feat_dim, hidden_dim, out_dim):
        m = tf.keras.models.Sequential()
        m.add(tf.keras.layers.Input(shape=(feat_dim,)))
        m.add(tf.keras.layers.Dense(hidden_dim, activation='relu'))
        m.add(tf.keras.layers.Dropout(0.5))
        m.add(tf.keras.layers.Dense(out_dim))
        return m