I have a TF Estimator that uses Feature Columns at its input layer. One of these is and EmbeddingColumn which I have been initializing randomly (the default behaviour).
Now I would like to pre-train my embeddings in gensim and transfer the learned embeddings into my TF model. The embedding_column accepts an initializer argument which expects a callable that can be created using tf.contrib.framework.load_embedding_initializer.
However, that function expects a saved TF checkpoint, which I don't have, because I trained my embeddings in gensim.
The question is: how do I save gensim word vectors (which are numpy arrays) as a tensor in the TF checkpoint format so that I can use that to initialize my embedding column?
Figured it out! This worked in Tensorflow 1.14.0.
You first need to turn the embedding vectors into a tf.Variable. Then use tf.train.Saver to save it in a checkpoint.
import tensorflow as tf
import numpy as np
ckpt_name = 'gensim_embeddings'
vocab_file = 'vocab.txt'
tensor_name = 'embeddings_tensor'
vocab = ['A', 'B', 'C']
embedding_vectors = np.array([
[1,2,3],
[4,5,6],
[7,8,9]
], dtype=np.float32)
embeddings = tf.Variable(initial_value=embedding_vectors)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver({tensor_name: embeddings})
with tf.Session() as sess:
sess.run(init_op)
saver.save(sess, ckpt_name)
# writing vocab file
with open(vocab_file, 'w') as f:
f.write('\n'.join(vocab))
To use this checkpoint to initialize an embedding feature column:
cat = tf.feature_column.categorical_column_with_vocabulary_file(
key='cat', vocabulary_file=vocab_file)
embedding_initializer = tf.contrib.framework.load_embedding_initializer(
ckpt_path=ckpt_name,
embedding_tensor_name='embeddings_tensor',
new_vocab_size=3,
embedding_dim=3,
old_vocab_file=vocab_file,
new_vocab_file=vocab_file
)
emb = tf.feature_column.embedding_column(cat, dimension=3, initializer=embedding_initializer, trainable=False)
And we can test to make sure it has been initialized properly:
def test_embedding(feature_column, sample):
feature_layer = tf.keras.layers.DenseFeatures(feature_column)
print(feature_layer(sample).numpy())
tf.enable_eager_execution()
sample = {'cat': tf.constant(['B', 'A'], dtype=tf.string)}
test_embedding(item_emb, sample)
The output, as expected, is:
[[4. 5. 6.]
[1. 2. 3.]]
Which are the embeddings for 'B' and 'A' respectively.
I define a customized loss function for my LSTM model (RMSE function) to be as follows:
def RMSE(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true)))
everything good so far, but the issue is that I scale my input data to be in the range of [-1, 1], so the reported loss will be associated with this scale, I want the model to report the training loss in the range of my original data, for example by applying the scaler.inverse_transform function on the y_true and y_pred somehow, but no luck doing it... as they are tensor and the scaler.inverse_transform requires numpy array....
any idea how to force re-scaling the data and reporting the loss values in the right scale?
scaler.inverse_transform essentially uses scaler.min_ and scaler.scale_ parameters to convert data in sklearn.preprocessing.minmaxscaler. An example:
from sklearn.preprocessing import MinMaxScaler
import numpy as np
data = np.array([[-1, 2], [-0.5, 6], [0, 10], [1, 18]])
scaler = MinMaxScaler()
data_trans = scaler.fit_transform(data)
print('transform:\n',data_trans)
data_inverse = (data_trans - scaler.min_)/scaler.scale_
print('inverse transform:\n',data_inverse)
# print
transform:
[[0. 0. ]
[0.25 0.25]
[0.5 0.5 ]
[1. 1. ]]
inverse transform:
[[-1. 2. ]
[-0.5 6. ]
[ 0. 10. ]
[ 1. 18. ]]
So you just need to use them to achieve your goals in RMSE function.
def RMSE_inverse(y_true, y_pred):
y_true = (y_true - K.constant(scaler.min_)) / K.constant(scaler.scale_)
y_pred = (y_pred - K.constant(scaler.min_)) / K.constant(scaler.scale_)
return K.sqrt(K.mean(K.square(y_pred - y_true)))
For the sake of example, I have an input consisting of 2 images,of total shape (2,299,299,3). I'm trying to apply inceptionv3 on each image, and then subsequently process the output with an LSTM. I'm using a masking layer to exclude a blank image from being processed (specified below).
The code is:
import numpy as np
from keras import backend as K
from keras.models import Sequential,Model
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, BatchNormalization, \
Input, GlobalAveragePooling2D, Masking,TimeDistributed, LSTM,Dense,Flatten,Reshape,Lambda, Concatenate
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.applications import inception_v3
IMG_SIZE=(299,299,3)
def create_base():
base_model = inception_v3.InceptionV3(weights='imagenet', include_top=False)
x = GlobalAveragePooling2D()(base_model.output)
base_model=Model(base_model.input,x)
return base_model
base_model=create_base()
#Image mask to ignore images with pixel values of -1
IMAGE_MASK = -2*np.expand_dims(np.ones(IMG_SIZE),0)
final_input=Input((2,IMG_SIZE[0],IMG_SIZE[1],IMG_SIZE[2]))
final_model = Masking(mask_value = -2.)(final_input)
final_model = TimeDistributed(base_model)(final_model)
final_model = Lambda(lambda x: x, output_shape=lambda s:s)(final_model)
#final_model = Reshape(target_shape=(2, 2048))(final_model)
#final_model = Masking(mask_value = 0.)(final_model)
final_model = LSTM(5,return_sequences=False)(final_model)
final_model = Model(final_input,final_model)
#Create a sample test image
TEST_IMAGE = np.ones(IMG_SIZE)
#Create a test sample input, consisting of a normal image and a masked image
TEST_SAMPLE = np.concatenate((np.expand_dims(TEST_IMAGE,axis=0),IMAGE_MASK))
inp = final_model.input # input placeholder
outputs = [layer.output for layer in final_model.layers] # all layer outputs
functors = [K.function([inp]+ [K.learning_phase()], [out]) for out in outputs]
layer_outs = [func([np.expand_dims(TEST_SAMPLE,0), 1.]) for func in functors]
This does not work correctly. Specifically, the model should mask the IMAGE_MASK part of the input, but it instead processes it with inception (giving a nonzero output). here are the details:
layer_out[-1] , the LSTM output is fine:
[array([[-0.15324114, -0.09620268, -0.01668587, 0.07938149, -0.00757846]], dtype=float32)]
layer_out[-2] and layer_out[-3] , the LSTM input is wrong, it should have all zeros in the second array:
[array([[[ 0.37713543, 0.36381325, 0.36197218, ..., 0.23298527,
0.43247852, 0.34844452],
[ 0.24972123, 0.2378867 , 0.11810347, ..., 0.51930511,
0.33289322, 0.33403745]]], dtype=float32)]
layer_out[-4], the input to the CNN is correctly masked:
[[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.],
...,
[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]]],
[[[-0., -0., -0.],
[-0., -0., -0.],
[-0., -0., -0.],
...,
[-0., -0., -0.],
[-0., -0., -0.],
[-0., -0., -0.]],
Note that the code seems to work correctly with a simpler base_model such as:
def create_base():
input_layer=Input(IMG_SIZE)
base_model=Flatten()(input_layer)
base_model=Dense(2048)(base_model)
base_model=Model(input_layer,base_model)
return base_model
I have exhausted most online resources on this. Permutations of this question have been asked on Keras's github, such as here, here and here, but I can't seem to find any concrete resolution.
The links suggest that the issues seem to be stemming from a combination of TimeDistributed being applied to BatchNormalization, and the hacky fixes of either the Lambda identity layer, or Reshape layers remove errors but don't seem to output the correct model.
I've tried to force the base model to support masking via:
base_model.__setattr__('supports_masking',True)
and I've also tried applying an identity layer via:
TimeDistributed(Lambda(lambda x: base_model(x), output_shape=lambda s:s))(final_model)
but none of these seem to work. Note that I would like the final model to be trainable, in particular the CNN part of it should remain trainable.
Not entirely sure this will work, but based on the comment made here, with a newer version of tensorflow + keras it should work:
final_model = TimeDistributed(Flatten())(final_input)
final_model = Masking(mask_value = -2.)(final_model)
final_model = TimeDistributed(Reshape(IMG_SIZE))(final_model)
final_model = TimeDistributed(base_model)(final_model)
final_model = Model(final_input,final_model)
I took a look at the source code of masking, and I noticed Keras creates a mask tensor that only reduces the last axis. As long as you're dealing with 5D tensors, it will cause no problem, but when you reduce the dimensions for the LSTM, this masking tensor becomes incompatible.
Doing the first flatten step, before masking, will assure that the masking tensor works properly for 3D tensors. Then you expand the image again to its original size.
I'll probably try to install newer versions soon to test it myself, but these installing procedures have caused too much trouble and I'm in the middle of something important here.
On my machine, this code compiles, but that strange error appears in prediction time (see link at the first line of this answer).
Creating a model for predicting the intermediate layers
I'm not sure, by the code I've seen, that the masking function is kept internally in tensors. I don't know exactly how it works, but it seems to be managed separately from the building of the functions inside the layers.
So, try using a keras standard model to make the predictions:
inp = final_model.input # input placeholder
outputs = [layer.output for layer in final_model.layers] # all layer outputs
fullModel = Model(inp,outputs)
layerPredictions = fullModel.predict(np.expand_dims(TEST_SAMPLE,0))
print(layerPredictions[-2])
It seems to be working as intended. Masking in Keras doesn't produce zeros as you would expect, it instead skips the timesteps that are masked in upstream layers such as LSTM and loss calculation. In case of RNNs, Keras (at least tensorflow) is implemented such that the states from the previous step are carried over, tensorflow_backend.py. This is done in part to preserve the shapes of tensors when dynamic input is given.
If you really want zeros you will have to implement your own layer with a similar logic to Masking and return zeros explicitly. To solve your problem, you need a mask before the final LSTM layer using the final_input:
class MyMask(Masking):
"""Layer that adds a mask based on initial input."""
def compute_mask(self, inputs, mask=None):
# Might need to adjust shapes
return K.any(K.not_equal(inputs[0], self.mask_value), axis=-1)
def call(self, inputs):
# We just return input back
return inputs[1]
def compute_output_shape(self, input_shape):
return input_shape[1]
final_model = MyMask(mask_value=-2.)([final_input, final_model])
You probably can attach the mask in a simpler manner but this custom class essentially adds a mask based on your initial inputs and outputs a Keras tensor that now has a mask.
Your LSTM will ignore in your example the second image. To confirm you can return_sequences=Trueand check that the output for 2 images are identical.
I'm trying implement the same thing, I want my LSTM sequences to have variable sizes. However I can't even implement your original model. I obtain the following error: TypeError: Layer input_1 does not support masking, but was passed an input_mask: Tensor("time_distributed_1/Reshape_1:0", shape=(?, 100, 100), dtype=bool) I'm using tensorflow 1.10 and keras 2.2.2
I solved the problem by adding a second input, a mask to specify which timesteps to take into account for the LSTM. That way the image sequence always has the same number of timesteps, the CNN always generates an output, but some of them are ignored for the LSTM input. However, the missing images need to be chosen carefully so that the batch normalization is not affected.
def LSTM_CNN(params):
resnet = ResNet50(include_top=False, weights='imagenet', pooling = 'avg')
input_layer = Input(shape=(params.numFrames, params.height, params.width, 3))
input_mask = Input(shape=(params.numFrames,1))
curr_layer = TimeDistributed(resnet)(input_layer)
resnetOutput = Dropout(0.5)(curr_layer)
curr_layer = multiply([resnetOutput,input_mask])
cnn_output = curr_layer
curr_layer = Masking(mask_value=0.0)(curr_layer)
lstm_out = LSTM(256, dropout=0.5)(curr_layer)
output = Dense(output_dim=params.numClasses, activation='sigmoid')(lstm_out)
model = Model([input_layer, input_mask], output)
return model
is there a small neural network in tf.nn.embedding_lookup??
When I train some data, a value of the same index is changing.
So is it trained also? while I'm training my model
I checked the official embedding_lookup code but I can not see any tf.Variables for train embedding parameter.
But when I print all tf.Variables then I can found a Variable which is within embedding scope
Thank you.
Yes, the embedding is learned. You can look at the tf.nn.embedding_lookup operation as doing the following matrix multiplication more efficiently:
import tensorflow as tf
import numpy as np
NUM_CATEGORIES, EMBEDDING_SIZE = 5, 3
y = tf.placeholder(name='class_idx', shape=(1,), dtype=tf.int32)
RS = np.random.RandomState(42)
W_em_init = RS.randn(NUM_CATEGORIES, EMBEDDING_SIZE)
W_em = tf.get_variable(name='W_em',
initializer=tf.constant_initializer(W_em_init),
shape=(NUM_CATEGORIES, EMBEDDING_SIZE))
# Using tf.nn.embedding_lookup
y_em_1 = tf.nn.embedding_lookup(W_em, y)
# Using multiplication
y_one_hot = tf.one_hot(y, depth=NUM_CATEGORIES)
y_em_2 = tf.matmul(y_one_hot, W_em)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
sess.run([y_em_1, y_em_2], feed_dict={y: [1.0]})
# [array([[ 1.5230298 , -0.23415338, -0.23413695]], dtype=float32),
# array([[ 1.5230298 , -0.23415338, -0.23413695]], dtype=float32)]
The variable W_em will be trained in exactly the same way irrespective of whether you use y_em_1 or y_em_2 formulation; y_em_1 is likely to be more efficient, though.