eustin

first_term_numerator = np.exp(score_obj_pos_1)
first_term_denominator = np.exp(score_obj_pos_1) + np.exp(score_obj_pos_2) + np.exp(score_obj_pos_3)

first_term = first_term_numerator / first_term_denominator

print(f"first term is {first_term}"

eustin

obj_pos_1, obj_pos_2, obj_pos_3 = pi

print(f"object at position 1 is '{obj_pos_1}'")
print(f"object at position 2 is '{obj_pos_2}'")
print(f"object at position 3 is '{obj_pos_3}'")

eustin

pi = random.choice(all_permutations)

print(pi)

eustin

scores_dict = {x: np.random.randn(1)[0] for x in ['shirt', 'pants', 'dress']}  

print(scores_dict)

eustin

all_permutations = list(itertools.permutations(objects_to_rank))

for x in sorted(all_permutations):
    print(x)

eustin

objects_to_rank = {'dress', 'shirt', 'pants'}

eustin

import random
random.seed(1)

import numpy as np
np.random.seed(1)

import itertools
import matplotlib.pyplot as plt
plt.style.use('ggplot')

eustin

loss_hist = []

for i in range(20):
    
    if i > 0:
        
        content_words, context_words, labels = make_skipgrams()
        
        hist = model.fit([content_words, context_words], labels, epochs=1, verbose=0)
        print(f"loss: {hist.history['loss'][-1]:.4f}")

eustin

# inputs
content_input = tf.keras.layers.Input(shape=(1, ), dtype=tf.int32, name='content_word')
context_input = tf.keras.layers.Input(shape=(1, ), dtype=tf.int32, name='context_word')

# layers
embeddings = tf.keras.layers.Embedding(input_dim=VOCAB_SIZE, output_dim=2, name='embeddings')
dot_prod = tf.keras.layers.Dot(axes=2, normalize=True, name='dot_product')
# graph
content_embedding = embeddings(content_input)
context_embedding = embeddings(context_input)

eustin

def make_skipgrams():
    train_x, all_labels = [], []
    for sequence in sequences:
        pairs, labels = tf.keras.preprocessing.sequence.skipgrams(
            sequence, VOCAB_SIZE, negative_samples=1.0, window_size=1, shuffle=True
        )
        train_x.extend(pairs)
        all_labels.extend(labels)

    train_x = np.array(train_x)