def parity(B=12, learning_rate=10e-5, epochs=200):
X, Y = all_parity_pairs_with_sequence_labels(B)
rnn = SimpleRNN(4)
rnn.fit(X,
Y,
learning_rate=learning_rate,
epochs=epochs,
activation=T.nnet.sigmoid,
show_fig=False)
def parity(B=12, learning_rate=10e-4, epochs=200):
X, Y_t = all_parity_pairs_with_sequence_labels(B)
rnn = SimpleRNN(20)
rnn.fit(X,
Y_t,
learning_rate=learning_rate,
epochs=epochs,
activation=T.nnet.relu,
show_fig=True)
def parity(B=12, learning_rate=1e-4, epochs=200): # B: number of bits
X, Y = all_parity_pairs_with_sequence_labels(B)
rnn = SimpleRNN(20) # size of hidden layer = 20
rnn.fit(X,
Y,
learning_rate=learning_rate,
epochs=epochs,
activation=T.nnet.relu,
show_fig=True)
def parity(B=12, learning_rate=1., epochs=1000):
X, Y = all_parity_pairs_with_sequence_labels(B)
rnn = SimpleRNN(4)
rnn.fit(X, Y,
batch_sz=len(Y),
learning_rate=learning_rate,
epochs=epochs,
activation=tf.nn.sigmoid,
show_fig=False
)
def parity(B=12, learning_rate=10e-4, epochs=1000):
X, Y = all_parity_pairs_with_sequence_labels(B)
rnn = SimpleRNN(4)
rnn.fit(X, Y,
batch_sz=10,
learning_rate=learning_rate,
epochs=epochs,
activation=tf.nn.sigmoid,
show_fig=False
)
def parity(B=12, learning_rate=1e-4, epochs=200):
X, Y = all_parity_pairs_with_sequence_labels(B)
X = X.astype(np.float32)
rnn = SimpleRNN(20)
rnn.fit(X,
Y,
learning_rate=learning_rate,
epochs=epochs,
activation=tf.nn.relu,
show_fig=False)
def parity(B=12, learning_rate=1., epochs=1000): # 很少看到learning rate 用 1
X, Y = all_parity_pairs_with_sequence_labels(B)
print('X.shape:', X.shape)
print('Y.shape:', Y.shape)
rnn = SimpleRNN(20)
rnn.fit(
X,
Y,
batch_sz=len(Y), #????用full batch???
learning_rate=learning_rate,
epochs=epochs,
activation=tf.nn.sigmoid,
show_fig=True)
>>>>>>> upstream/master
break
costs.append(cost)
if show_fig:
plt.plot(costs)
plt.show()
<<<<<<< HEAD
def parity(B=12, learning_rate=10e-4, epochs=1000):
=======
def parity(B=12, learning_rate=1., epochs=1000):
>>>>>>> upstream/master
X, Y = all_parity_pairs_with_sequence_labels(B)
rnn = SimpleRNN(4)
rnn.fit(X, Y,
<<<<<<< HEAD
batch_sz=10,
=======
batch_sz=len(Y),
>>>>>>> upstream/master
learning_rate=learning_rate,
epochs=epochs,
activation=tf.nn.sigmoid,
show_fig=False
)
def parity(B=12, learning_rate=1e-4, epochs=200):
X, Y = all_parity_pairs_with_sequence_labels(B)
rnn = SimpleRNN(20)
rnn.fit(X, Y, learning_rate=learning_rate, epochs=epochs, activation=T.nnet.relu, show_fig=False)
def parity(B=12, learning_rate=1., epochs=1000):
X, Y = all_parity_pairs_with_sequence_labels(B)
N, T, D = X.shape
rnn = BasicRNN(X, 4)
rnn.fit(X, Y, batch_size=100, epochs=1000)
