Tensorflow with Jupyterhub

import tensorflow as tf def download_tiny_shakespeare(): path = tf.keras.utils.get_file( "shakespeare.txt", "https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt", ) with open(path, "r", encoding="utf-8") as f: text = f.read() return text def build_dataset(text, seq_length=100, batch_size=64, buffer_size=10000): vocab = sorted(set(text)) char2idx = {u: i for i, u in enumerate(vocab)} idx2char = tf.constant(vocab) text_as_int = tf.constant([char2idx[c] for c in text], dtype=tf.int32) ds = tf.data.Dataset.from_tensor_slices(text_as_int) sequences = ds.batch(seq_length + 1, drop_remainder=True) def split_input_target(chunk): return chunk[:-1], chunk[1:] ds = sequences.map(split_input_target, num_parallel_calls=tf.data.AUTOTUNE) ds = ds.shuffle(buffer_size).batch(batch_size, drop_remainder=True).prefetch(tf.data.AUTOTUNE) return ds, char2idx, idx2char, len(vocab) def build_model(vocab_size, embedding_dim=256, rnn_units=512, batch_size=64): return tf.keras.Sequential([ tf.keras.layers.Input(batch_shape=(batch_size, None), dtype=tf.int32), tf.keras.layers.Embedding(vocab_size, embedding_dim), tf.keras.layers.GRU( rnn_units, return_sequences=True, stateful=True, recurrent_initializer="glorot_uniform", ), tf.keras.layers.Dense(vocab_size), ]) def generate_text(model, start_string, char2idx, idx2char, num_generate=600, temperature=0.9): input_eval = tf.expand_dims([char2idx.get(s, 0) for s in start_string], 0) text_generated = [] for layer in model.layers: if hasattr(layer, "reset_states"): layer.reset_states() for _ in range(num_generate): predictions = model(input_eval) # (1, time, vocab) predictions = predictions[:, -1, :] # (1, vocab) predictions = predictions / temperature predicted_id = tf.random.categorical(predictions, num_samples=1)[0, 0].numpy() input_eval = tf.expand_dims([predicted_id], 0) text_generated.append(idx2char[predicted_id].numpy().decode("utf-8")) return start_string + "".join(text_generated) def main(): tf.get_logger().setLevel("ERROR") print("TensorFlow:", tf.__version__) text = download_tiny_shakespeare() dataset, char2idx, idx2char, vocab_size = build_dataset(text, seq_length=100, batch_size=64) model = build_model(vocab_size, embedding_dim=256, rnn_units=512, batch_size=64) model.compile( optimizer=tf.keras.optimizers.Adam(1e-3), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), ) model.fit(dataset, epochs=5) gen_model = build_model(vocab_size, embedding_dim=256, rnn_units=512, batch_size=1) gen_model.set_weights(model.get_weights()) print("\n--- Generated monologue ---\n") print(generate_text( gen_model, start_string="KING HENRY:\n", char2idx=char2idx, idx2char=idx2char, num_generate=700, temperature=0.85, )) if __name__ == "__main__": main()

# To get TF_CPP_MIN_LOG_LEVEL to work, you must set it before importing tensorflow import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import tensorflow as tf import warnings warnings.filterwarnings("ignore", category=UserWarning)