cross_entropy = -tf.reduce_sum(output_label_*tf.log(output_label))
# get gradients and clip them, use adaptive to prevent explosions
tvars = tf.trainable_variables()
grads = [tf.clip_by_value(grad, -2., 2.) for grad in tf.gradients(cross_entropy, tvars)]
optimizer = tf.train.AdagradOptimizer(0.01)
train_step = optimizer.apply_gradients(zip(grads, tvars))
# =============== TRAINING AND TESTING ================== #
# initialize
sess.run([tf.variables.initialize_all_variables()])
# run over many epochs
for i in range(50001):
# get data dynamically from my data generator
dat, lab = dg.gen_data_batch(batch_size, example_length)
# do evaluation every 100 epochs
if (i % 100 == 0):
print("====current accuracy==== at epoch ", i)
pos_data, pos_label = dg.gen_data_batch(100, example_length, pos_neg = True)
neg_data, neg_label = dg.gen_data_batch(100, example_length, pos_neg = False)
correct_prediction = tf.equal(tf.argmax(output_label,1), tf.argmax(output_label_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
res = sess.run([accuracy], feed_dict={input_seq: pos_data, output_label_: pos_label})
print("pos accuracy: ", res)
res = sess.run([accuracy], feed_dict={input_seq: neg_data, output_label_: neg_label})
print("neg accuracy: ", res)
# continuously train at every epoch
sess.run(train_step, feed_dict={input_seq: dat, output_label_: lab})
num_epochs = 2000
inputs = keras.Input(shape=(length, input_size), dtype=tf.float32)
lstm = keras.layers.LSTM(num_units,
input_shape=(None, length, input_size))(inputs)
relu = keras.layers.Dense(hidden_units, activation=tf.nn.relu)(lstm)
out = keras.layers.Dense(label_size, activation=tf.nn.softmax)(relu)
model = keras.Model(inputs=inputs, outputs=out)
model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
for i in range(num_epochs):
data, label = dg.gen_data_batch(batch_size, length)
if (i % 100 == 0):
print("\ncurrent_accuracy at epoch ", i)
pos_data, pos_label = dg.gen_data_batch(100, length, True)
neg_data, neg_label = dg.gen_data_batch(100, length, False)
loss1, accuracy1 = model.evaluate(pos_data,
pos_label,
batch_size=batch_size)
loss2, accuracy2 = model.evaluate(neg_data,
neg_label,
batch_size=batch_size)
print("\nPositive Loss: %.3f\n Positive Acc: %.3f" %
(loss1, accuracy1))
print("\nNegative Loss: %.3f\n Negative Acc: %.3f" %
(loss2, accuracy2))
model.fit(data, label, epochs=1, batch_size=batch_size)
w2 = tf.Variable(tf.random_normal([hidden_units, label_size], mean=0.1, stddev=0.035))
b2 = tf.Variable(tf.zeros([label_size]))
output_label = tf.nn.softmax(tf.matmul(relu1, w2) + b2)
# minimize cross entropy against the true label
cross_entropy = -tf.reduce_sum(output_label_*tf.log(output_label))
# get gradients and clip them, use adaptive to prevent explosions
tvars = tf.trainable_variables()
grads = [tf.clip_by_value(grad, -2., 2.) for grad in tf.gradients(cross_entropy, tvars)]
optimizer = tf.train.AdagradOptimizer(0.01)
train_step = optimizer.apply_gradients(zip(grads, tvars))
# =============== TRAINING AND TESTING ================== #
# initialize
sess.run([tf.variables.initialize_all_variables()])
# run over many epochs
for i in range(50001):
# get data dynamically from my data generator
dat, lab = dg.gen_data_batch(batch_size, example_length)
# do evaluation every 100 epochs
if (i % 100 == 0):
print("====current accuracy==== at epoch ", i)
correct_prediction = tf.equal(tf.argmax(output_label,1), tf.argmax(output_label_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
res = sess.run([accuracy], feed_dict={input_seq: dat, output_label_: lab})
print(res)
# continuously train at every epoch
sess.run(train_step, feed_dict={input_seq: dat, output_label_: lab})
