ial demo of the ChatGLM-6B model, trained on 1T tokens of English and Chinese

Chatbot
Please create application in python, text to image AI
Sure, here is an example of a text-to-image AI application in Python:

import tensorflow as tf import numpy as np # Define the model model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ]) # Compile the model model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Train the model on a dataset data = np.array([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 1]]) model.fit(data, epochs=10, validation_data=(data, data)) # Evaluate the model on a validation dataset validation_loss, validation_accuracy = model.evaluate(data, epochs=5) # Convert the model's predictions to images predictions = model.predict(np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]])) # Convert the predictions to binary images predictions = np.astype(np.uint8) * 255 predictions = np.hstack([predictions, np.zeros((28, 28))]) # Create a 2D array with the predicted labels predicted_labels = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]]) # Create a 3D array with the actual images and their labels actual_images = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]]) # Create a 3D array with the sum of the predicted labels and the actual images total_loss = np.sum(predicted_labels, axis=0) total_accuracy = np.sum(predicted_labels == actual_images, axis=0) # Print the results print('Total loss:', total_loss) print('Accuracy:', total_accuracy)
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ial demo of the ChatGLM-6B model, trained on 1T tokens of English and Chinese

Chatbot
Please create application in python, text to image AI
Sure, here is an example of a text-to-image AI application in Python:

import tensorflow as tf import numpy as np # Define the model model = URL.Sequential([ URL.Flatten(input_shape=(28, 28)), URL.Dense(128, activation='relu'), URL.Dense(10, activation='softmax') ]) # Compile the model model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Train the model on a dataset data = URL([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 1]]) URL(data, epochs=10, validation_data=(data, data)) # Evaluate the model on a validation dataset validation_loss, validation_accuracy = model.evaluate(data, epochs=5) # Convert the model's predictions to images predictions = model.predict(URL([[0, 0, 0], [1, 1, 1], [0, 0, 0]])) # Convert the predictions to binary images predictions = URL(np.uint8) * 255 predictions = URL([predictions, URL((28, 28))]) # Create a 2D array with the predicted labels predicted_labels = URL([[0, 0, 0], [1, 1, 1], [0, 0, 0]]) # Create a 3D array with the actual images and their labels actual_images = URL([[0, 0, 0], [1, 1, 1], [0, 0, 0]]) # Create a 3D array with the sum of the predicted labels and the actual images total_loss = URL(predicted_labels, axis=0) total_accuracy = URL(predicted_labels == actual_images, axis=0) # Print the results print('Total loss:', total_loss) print('Accuracy:', total_accuracy)
# Define the model model = URL.Sequential([ URL.Flatten(input_shape=(28, 28)), URL.Dense(128, activation='relu'), URL.Dense(10, activation='softmax') ]) # Compile the model model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Train the model on a dataset data = URL([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 1]]) URL(data, epochs=10, validation_data=(data, data)) # Evaluate the model on a validation dataset validation_loss, validation_accuracy = model.evaluate(data, epochs=5) # Convert the model's predictions to images predictions = model.predict(URL([[0, 0, 0], [1, 1, 1], [0, 0, 0]])) # Convert the predictions to binary images predictions = URL(np.uint8) * 255 predictions = URL([predictions, URL((28, 28))]) # Create a 2D array with the predicted labels predicted_labels = URL([[0, 0, 0], [1, 1, 1], [0, 0, 0]]) # Create a 3D array with the actual images and their labels actual_images = URL([[0, 0, 0], [1, 1, 1], [0, 0, 0]]) # Create a 3D array with the sum of the predicted labels and the actual images total_loss = URL(predicted_labels, axis=0) total_accuracy = URL(predicted_labels == actual_images, axis=0) # Print the results print('Total loss:', total_loss) print('Accuracy:', total_accuracy)
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 # Define the model model = URL.Sequential([ URL.Flatten(input_shape=(28, 28)), URL.Dense(128, activation='relu'), URL.Dense(10, activation='softmax') ]) # Compile the model model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Train the model on a dataset data = URL([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 1]]) URL(data, epochs=10, validation_data=(data, data)) # Evaluate the model on a validation dataset validation_loss, validation_accuracy = model.evaluate(data, epochs=5) # Convert the model's predictions to images predictions = model.predict(URL([[0, 0, 0], [1, 1, 1], [0, 0, 0]])) # Convert the predictions to binary images predictions = URL(np.uint8) * 255 predictions = URL([predictions, URL((28, 28))]) # Create a 2D array with the predicted labels predicted_labels = URL([[0, 0, 0], [1, 1, 1], [0, 0, 0]]) # Create a 3D array with the actual images and their labels actual_images = URL([[0, 0, 0], [1, 1, 1], [0, 0, 0]]) # Create a 3D array with the sum of the predicted labels and the actual images total_loss = URL(predicted_labels, axis=0) total_accuracy = URL(predicted_labels == actual_images, axis=0) # Print the results print('Total loss:', total_loss) print('Accuracy:', total_accuracy)