the same model converged in keras but not tensorflow, how is that possible? - tensorflow

Question

I'm trying to work with lstm in tensorflow, but I got to the point I can't make a simple imdb sentiment model to converge.
I took a keras model and tried to duplicate the exact same model in tensorflow, in keras it trains and converge however in tensorflow it is just stuck at some point (0.69 loss).
I tried to make them as equal as possible, the only difference I can tell of is that in keras the padding is before the sequence, while in tensorflow I use 'post' padding due to the conventions in tensorflow.
Any idea whats wrong with my tensorflow model?
from __future__ import print_function
import random
import numpy as np
from tensorflow.contrib.keras.python.keras.preprocessing import sequence
from tensorflow.contrib.keras.python.keras.models import Sequential
from tensorflow.contrib.keras.python.keras.layers import Dense, Dropout, Activation
from tensorflow.contrib.keras.python.keras.layers import Embedding
from tensorflow.contrib.keras.python.keras.layers import LSTM
from tensorflow.contrib.keras.python.keras.layers import Conv1D, MaxPooling1D
from tensorflow.contrib.keras.python.keras.datasets import imdb
import tensorflow as tf
# Embedding
max_features = 30000
maxlen = 2494
embedding_size = 128
# Convolution
kernel_size = 5
filters = 64
pool_size = 4
# LSTM
lstm_output_size = 70
# Training
batch_size = 30
epochs = 2
class TrainData:
def __init__(self, batch_sz=batch_size):
(x_train, y_train), (_, _) = imdb.load_data(num_words=max_features)
y_train = [[int(x == 1), int(x != 1)] for x in y_train]
self._batch_size = batch_sz
self._train_data = sequence.pad_sequences(x_train, padding='pre')
self._train_labels = y_train
def next_batch(self):
if len(self._train_data) < self._batch_size:
self.__init__()
batch_x, batch_y = self._train_data[:self._batch_size], self._train_labels[:self._batch_size]
self._train_data = self._train_data[self._batch_size:]
self._train_labels = self._train_labels[self._batch_size:]
return batch_x, batch_y
def batch_generator(self):
while True:
if len(self._train_data) < self._batch_size:
self.__init__()
batch_x, batch_y = self._train_data[:self._batch_size], self._train_labels[:self._batch_size]
self._train_data = self._train_data[self._batch_size:]
self._train_labels = self._train_labels[self._batch_size:]
yield batch_x, batch_y
def get_num_batches(self):
return int(len(self._train_data) / self._batch_size)
def length(sequence):
used = tf.sign(tf.abs(sequence))
length = tf.reduce_sum(used, reduction_indices=1)
length = tf.cast(length, tf.int32)
return length
def get_model(x, y):
embedding = tf.get_variable("embedding", [max_features, embedding_size], dtype=tf.float32)
embedded_x = tf.nn.embedding_lookup(embedding, x)
print(x)
print(embedded_x)
print(length(x))
cell_1 = tf.contrib.rnn.BasicLSTMCell(lstm_output_size)
output_1, state_1 = tf.nn.dynamic_rnn(cell_1, embedded_x, dtype=tf.float32, scope="rnn_layer1",
sequence_length=length(x))
# Select last output.
last_index = tf.shape(output_1)[1] - 1
# reshaping to [seq_length, batch_size, num_units]
output = tf.transpose(output_1, [1, 0, 2])
last = tf.gather(output, last_index)
# Softmax layer
with tf.name_scope('fc_layer'):
weight = tf.get_variable(name="weights", shape=[lstm_output_size, 2])
bias = tf.get_variable(shape=[2], name="bias")
logits = tf.matmul(last, weight) + bias
loss = tf.losses.softmax_cross_entropy(y, logits=logits)
optimizer = tf.train.AdamOptimizer()
optimize_step = optimizer.minimize(loss=loss)
return loss, optimize_step
def tf_model():
x_holder = tf.placeholder(tf.int32, shape=[None, maxlen])
y_holder = tf.placeholder(tf.int32, shape=[None, 2])
loss, opt_step = get_model(x_holder, y_holder)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step = 0
for epoch in range(10):
cost_epochs = []
train_data = TrainData()
cost_batch = 0
for batch in range(train_data.get_num_batches()):
x_train, y_train = train_data.next_batch()
_, cost_batch = sess.run([opt_step, loss],
feed_dict={x_holder: x_train,
y_holder: y_train})
cost_epochs.append(cost_batch)
step += 1
# if step % 100 == 0:
print("Epoch: " + str(epoch))
print("\tcost: " + str(np.mean(cost_epochs)))
def keras_model():
# print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
y_test = [[int(x == 1), int(x != 1)] for x in y_test]
x_test = sequence.pad_sequences(x_test, maxlen=maxlen, padding='pre')
model = Sequential()
model.add(Embedding(max_features, embedding_size, input_length=maxlen))
model.add(LSTM(lstm_output_size))
model.add(Dense(2))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print('Train...')
data = TrainData()
model.fit_generator(data.batch_generator(), steps_per_epoch=data.get_num_batches(),
epochs=epochs,
validation_data=(x_test, y_test))
if __name__ == '__main__':
# keras_model()
tf_model()
EDIT
When I limit the sequence length to 100 both models converge, so I assume there is something different in the the lstm layer.

Answer 1

Check the initial values of your operations. In my case the adadelta optimizer in keras had initial learning rate of 1.0 and in tf.keras it had 0.001 so in the mnist dataset it converged much slowly.