[tf] rnn project

conda 환경을 새로 만들고 싶지만 시간이 아까워서 900gle 의 환경을 사용.

conda envs

conda activate 900gle

 

 

python -m pip install --upgrade pip

 

 

 

Study.py

import numpy as np

import tensorflow_datasets as tfds
import tensorflow as tf

tfds.disable_progress_bar()

    "This version of TensorFlow Datasets requires TensorFlow "
ImportError: This version of TensorFlow Datasets requires TensorFlow version >= 2.1.0; Detected an installation of version 1.14.0. Please upgrade TensorFlow to proceed.

 

에라이 ..

가상환경 다시 만듦

 

conda create --name "rnn" python="3.7"

 

 

 

 

 

 

 

import matplotlib.pyplot as plt
import numpy as np

import tensorflow_datasets as tfds
import tensorflow as tf

tfds.disable_progress_bar()


def plot_graphs(history, metric):
    plt.plot(history.history[metric])
    plt.plot(history.history['val_'+metric],'')
    plt.xlabel("Epochs")
    plt.ylabel(metric)
    plt.legend([metric, 'val_'+metric])


dataset, info = tfds.load('imdb_reviews', with_info=True, as_supervised=True)
train_dataset, test_dataset = dataset['train'], dataset['test']

for example, label in train_dataset.take(1):
    print('text : ', example.numpy())
    print('label : ', label.numpy())


BUFFER_SIZE = 10000
BATCH_SIZE = 64

train_dataset = train_dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
test_dataset = test_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)

for example, label in train_dataset.take(1):
    print('text : ', example.numpy()[:3])
    print()
    print('label : ', label.numpy()[:3])

VOCAB_SIZE=1000
encoder = tf.keras.layers.experimental.preprocessing.TextVectorization(max_tokens=VOCAB_SIZE)
encoder.adapt(train_dataset.map(lambda text, label: text))

vocab = np.array(encoder.get_vocabulary())
vocab[:20]


encoded_example = encoder(example)[:3].numpy()
encoded_example


tf.keras.layers.LSTM(
    units, activation='tanh', recurrent_activation='sigmoid',
    use_bias=True, kernel_initializer='glorot_uniform',
    recurrent_initializer='orthogonal',
    bias_initializer='zeros', unit_forget_bias=True,
    kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None,
    activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None,
    bias_constraint=None, dropout=0.0, recurrent_dropout=0.0,
    return_sequences=False, return_state=False, go_backwards=False, stateful=False,
    time_major=False, unroll=False, **kwargs
)


model = tf.keras.Sequential([[
    encoder,
    tf.keras.layers.Embedding(
        input_dim=len(encoder.get_vocabulary()),
        output_dim=64,
        mask_zero=True
    ),
    tf.keras.layers.LSTM(64),
    tf.keras.layers.Dense(64,activation='relu'),
    tf.keras.layers.Dense(1)
]])


model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),optimizer=tf.optimizers.Adam(1e-4),
              metrics=['accuracy'])

history = model.fit(train_dataset, epochs=3, validation_data=test_dataset, validation_steps=30)

test_loss, test_acc = model.evaluate(test_dataset)
print('Test Loss: {}'.format(test_loss))
print('Test Accuracy: {}'.format(test_acc))

plt.figure(figsize=(16,8))
plt.subplot(1,2,1)
plot_graphs(history, 'accuracy')
plt.ylim(None, 1)
plt.subplot(1,2,2)
plot_graphs(history, 'loss')
plt.ylim(0,None)