def main():
(X_train, y_train), (X_test,
y_test) = tf.contrib.keras.datasets.mnist.load_data()
X_train = X_train / 255.
X_test = X_test / 255.
model = RNNClassifier(mx.cpu(), n_out=10)
model.fit(X_train, y_train)
pred = model.predict(X_test)
final_acc = (pred == y_test).mean()
print("final testing accuracy: %.4f" % final_acc)
def main():
(X_train,
y_train), (X_test,
y_test) = tf.contrib.keras.datasets.cifar10.load_data()
X_train = (X_train / 255.).mean(axis=3)
X_test = (X_test / 255.).mean(axis=3)
y_train = y_train.ravel()
y_test = y_test.ravel()
model = RNNClassifier(mx.cpu(), n_out=10)
model.fit(X_train, y_train)
pred = model.predict(X_test)
final_acc = (pred == y_test).mean()
print("final testing accuracy: %.4f" % final_acc)
from rnn_clf import RNNClassifier
import numpy as np
import tensorflow as tf
if __name__ == '__main__':
(X_train, y_train), (X_test, y_test) = tf.contrib.keras.datasets.mnist.load_data()
X_train = X_train / 255.0
X_test = X_test / 255.0
Y_train = tf.contrib.keras.utils.to_categorical(y_train)
Y_test = tf.contrib.keras.utils.to_categorical(y_test)
clf = RNNClassifier(n_in=28, n_out=10, stateful=True)
log = clf.fit(X_train, y_train, keep_prob_tuple=(0.8,1.0), val_data=(X_test, y_test))
pred = clf.predict(X_test)
final_acc = (pred == y_test).mean()
print("final testing accuracy: %.4f" % final_acc)
from rnn_clf import RNNClassifier
import numpy as np
import tensorflow as tf
if __name__ == '__main__':
(X_train,
y_train), (X_test,
y_test) = tf.contrib.keras.datasets.cifar10.load_data()
X_train = (X_train / 255.0).mean(axis=3)
X_test = (X_test / 255.0).mean(axis=3)
clf = RNNClassifier(n_in=32, n_step=32, n_out=10)
log = clf.fit(X_train, y_train, val_data=(X_test, y_test))
pred = clf.predict(X_test)
final_acc = (pred == y_test).mean()
print("final testing accuracy: %.4f" % final_acc)
from rnn_clf import RNNClassifier
import tensorflow as tf
n_in = 28
cell_size = 128
n_layer = 2
n_out = 10
batch_size = 128
n_epoch = 1
if __name__ == '__main__':
(X_train, y_train), (X_test, y_test) = tf.contrib.keras.datasets.mnist.load_data()
X_train = X_train / 255.0
X_test = X_test / 255.0
rnn = RNNClassifier(n_in, n_out, cell_size, n_layer)
rnn.fit(X_train, y_train, n_epoch, batch_size)
rnn.evaluate(X_test, y_test, batch_size)
#
# linear_output = linear(final_output)
# print("Shape of linear output: ", linear_output.shape)
#
# softmax_output = softmax(linear_output)
# print("Shape of softmax output: ", softmax_output.shape)
# print("Shape of target: ", y.shape)
#
# loss = criterion(softmax_output, y)
# print("Loss value: ", loss.data.numpy())
from rnn_clf import RNNClassifier
model = RNNClassifier(vocal_size=vocal_size,
embedding_dim=100,
hidden_dim=50,
output_dim=label_size,
batch_size=1)
optim = SGD(params=model.parameters(), lr=0.01)
criterion = NLLLoss()
for i in range(10):
total_loss = 0
model.train()
for it, ex in enumerate(train_data):
f, t = ex
X = torch.LongTensor(f)
y = torch.LongTensor(t)
model.hidden = model.init_hidden()
output = model.forward(X)