TensorFlow is an open-source programming library for machine learning and deep learning. It is developed by Google and is one of the most popular tools for creating and training machine learning models. TensorFlow allows for the creation and training of models using various techniques, such as neural networks, decision trees, and regression. The library can be used for classification, segmentation, image recognition, speech recognition, and many other applications in the field of artificial intelligence.
In Shopzzy AI, we use TensorFlow to train models that can recognize and analyze patterns in data, which allows for improving our recommendation algorithms, personalized shopping suggestions, and other intelligent features that provide users with better and more tailored product recommendations. Thanks to TensorFlow, we are able to create models that learn from data and can predict customers' preferences and needs, resulting in even more precise and effective e-commerce solutions. By using TensorFlow in Shopzzy AI, we aim to continuously improve our algorithms and models, in order to offer our users the best and most intuitive online shopping experience possible.
This code trains a neural network on the sample data, which consists of product names and categories. The neural network is then used to classify a new product based on its name. The model uses an LSTM layer for natural language processing, an embedding layer for word representation, and two fully connected layers for classification. The code uses the Keras API for TensorFlow to build and train the model.
// Simple Shopzzy AI script utilizing TensorFlow technology
import tensorflow as tf
import numpy as np
# Sample data
train_data = [
("Nike Soccer Ball", "sports"),
("The North Face Winter Jacket", "outdoor"),
("Samsung Galaxy Smartphone", "electronics"),
("Lavazza Coffee Beans", "food"),
("Asics Running Shoes", "sports"),
("GoPro Camera", "electronics"),
("Deuter Trekking Backpack", "outdoor"),
("Garmin Sports Watch", "sports")
]
# Prepare training data
train_x = [x[0] for x in train_data]
train_y = [x[1] for x in train_data]
# Tokenize input data
tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(train_x)
train_sequences = tokenizer.texts_to_sequences(train_x)
# Pad input data
train_padded = tf.keras.preprocessing.sequence.pad_sequences(train_sequences)
# Convert output data to one-hot encoding
train_labels = np.zeros((len(train_y), len(set(train_y))))
for i, label in enumerate(train_y):
train_labels[i, train_y.index(label)] = 1
# Neural network model
model = tf.keras.Sequential([
tf.keras.layers.Embedding(len(tokenizer.word_index)+1, 64),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(len(set(train_y)), activation='softmax')
])
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Train the model
model.fit(train_padded, train_labels, epochs=10)
# Sample product description
product_description = "Adidas Soccer Ball"
# Tokenize and pad product description
product_sequence = tokenizer.texts_to_sequences([product_description])
product_padded = tf.keras.preprocessing.sequence.pad_sequences(product_sequence)
# Classify product
class_index = np.argmax(model.predict(product_padded), axis=-1)[0]
predicted_class = list(set(train_y))[class_index]
# Print result
print(f"Product description: {product_description}")
print(f"Predicted category: {predicted_class}")
