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Using the TFBertForQuestionAnswering.from_pretrained() function, we get a predefined head on top of BERT together with a loss function that are suitable for this task.
My question is how to create a custom head without relying on TFAutoModelForQuestionAnswering.from_pretrained().
I want to do this because there is no place where the architecture of the head is explained clearly. By reading the code here we can see the architecture they are using, but I can't be sure I understand their code 100%.
Starting from How to Fine-tune HuggingFace BERT model for Text Classification is good. However, it covers only the classification task, which is much simpler.
'start_positions' and 'end_positions' are created following this tutorial.
So far, I've got the following:
train_dataset
# Dataset({
# features: ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions'],
# num_rows: 99205
# })
train_dataset.set_format(type='tensorflow', columns=['input_ids', 'token_type_ids', 'attention_mask'])
features = {x: train_dataset[x] for x in ['input_ids', 'token_type_ids', 'attention_mask']}
labels = [train_dataset[x] for x in ['start_positions', 'end_positions']]
labels = np.array(labels).T
tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(16)
input_ids = tf.keras.layers.Input(shape=(256,), dtype=tf.int32, name='input_ids')
token_type_ids = tf.keras.layers.Input(shape=(256,), dtype=tf.int32, name='token_type_ids')
attention_mask = tf.keras.layers.Input((256,), dtype=tf.int32, name='attention_mask')
bert = TFAutoModel.from_pretrained("bert-base-multilingual-cased")
output = bert([input_ids, token_type_ids, attention_mask]).last_hidden_state
output = tf.keras.layers.Dense(2, name="qa_outputs")(output)
model = tf.keras.models.Model(inputs=[input_ids, token_type_ids, attention_mask], outputs=output)
num_train_epochs = 3
num_train_steps = len(tfdataset) * num_train_epochs
optimizer, schedule = create_optimizer(
init_lr=2e-5,
num_warmup_steps=0,
num_train_steps=num_train_steps,
weight_decay_rate=0.01
)
def qa_loss(labels, logits):
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE
)
start_loss = loss_fn(labels[0], logits[0])
end_loss = loss_fn(labels[1], logits[1])
return (start_loss + end_loss) / 2.0
model.compile(
loss=loss_fn,
optimizer=optimizer
)
model.fit(tfdataset, epochs=num_train_epochs)
And I am getting the following error:
ValueError: `labels.shape` must equal `logits.shape` except for the last dimension. Received: labels.shape=(2,) and logits.shape=(256, 2)
It is complaining about the shape of the labels. This should not happen since I am using SparseCategoricalCrossentropy loss.
For future reference, I actually found a solution, which is just editing the TFBertForQuestionAnswering class itself. For example, I added an additional layer in the following code and trained the model as usual and it worked.
from transformers import TFBertPreTrainedModel
from transformers import TFBertMainLayer
from transformers.modeling_tf_utils import TFQuestionAnsweringLoss, get_initializer, input_processing
from transformers.modeling_tf_outputs import TFQuestionAnsweringModelOutput
from transformers import BertConfig
class MY_TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"mlm___cls",
r"nsp___cls",
r"cls.predictions",
r"cls.seq_relationship",
]
def __init__(self, config: BertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, add_pooling_layer=False, name="bert")
# This is the dense layer I added
self.my_dense = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="my_dense",
)
self.qa_outputs = tf.keras.layers.Dense(
units=config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="qa_outputs",
)
def call(
self,
input_ids = None,
attention_mask = None,
token_type_ids = None,
position_ids = None,
head_mask = None,
inputs_embeds = None,
output_attentions = None,
output_hidden_states = None,
return_dict = None,
start_positions = None,
end_positions= None,
training = False,
**kwargs,
):
r"""
start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
start_positions=start_positions,
end_positions=end_positions,
training=training,
kwargs_call=kwargs,
)
outputs = self.bert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = outputs[0]
# You also have to add it here
my_logits = self.my_dense(inputs=sequence_output)
logits = self.qa_outputs(inputs=my_logits)
start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
start_logits = tf.squeeze(input=start_logits, axis=-1)
end_logits = tf.squeeze(input=end_logits, axis=-1)
loss = None
if inputs["start_positions"] is not None and inputs["end_positions"] is not None:
labels = {"start_position": inputs["start_positions"]}
labels["end_position"] = inputs["end_positions"]
loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))
if not inputs["return_dict"]:
output = (start_logits, end_logits) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFQuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFQuestionAnsweringModelOutput(
start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
)
I'm trying to write a custom layer that will handle variable-length vectors, and reduce them to the same length vector.
The length is known in advance because the reason for the variable lengths is that I have several different data types that I encode using a different number of features.
In a sense, it is similar to Embedding only for numerical values.
I've tried using padding, but the results were bad, so I'm trying this approach instead.
So, for example let's say I have 3 data types, which I encode with 3, 4, 6 length vectors.
arr = [
# example one (data type 1 [len()==3], datat type 3[len()==6]) - force values as floats
[[1.0,2.0,3],[1,2,3,4,5,6]],
# example two (data type 2 [len()==4], datat type 3len()==6]) - force values as floats
[[1.0,2,3,4],[1,2,3,4,5,6]],
]
I tried implementing a custom layer like:
class DimensionReducer(tf.keras.layers.Layer):
def __init__(self, output_dim, expected_lengths):
super(DimensionReducer, self).__init__()
self._supports_ragged_inputs = True
self.output_dim = output_dim
for l in expected_lengths:
setattr(self,f'w_{l}', self.add_weight(shape=(l, self.output_dim),initializer='random_normal',trainable=True))
setattr(self, f'b_{l}',self.add_weight(shape=(self.output_dim,), initializer='random_normal',trainable=True))
def call(self, inputs):
print(inputs.shape)
# batch
if len(inputs.shape) == 3:
print("batch")
result = []
for i,x in enumerate(inputs):
_result = []
for v in x:
l = len(v)
print(l)
print(v)
w = getattr(self, f'w_{l}')
b = getattr(self, f'b_{l}')
out = tf.matmul([v],w) + b
_result.append(out)
result.append(tf.concat(_result, 0))
r = tf.stack(result)
print("batch output:",r.shape)
return r
Which seems to be working when called directly:
dim = DimensionReducer(3, [3,4,6])
dim(tf.ragged.constant(arr))
But when I try to incorporate it into a model, it fails:
import tensorflow as tf
val_ragged = tf.ragged.constant(arr)
inputs_ragged = tf.keras.layers.Input(shape=(None,None), ragged=True)
outputs_ragged = DimensionReducer(3, [3,4,6])(inputs_ragged)
model_ragged = tf.keras.Model(inputs=inputs_ragged, outputs=outputs_ragged)
# this one with RaggedTensor doesn't
print(model_ragged(val_ragged))
With
AttributeError: 'DimensionReducer' object has no attribute 'w_Tensor("dimension_reducer_98/strided_slice:0", shape=(), dtype=int32)'
I'm not sure how am I to implement such a layer, or what I'm doing wrong.
I am trying to train a triple loss model using a fit_generator. it requires three input and no output. so i have a function that generates hard triplets. the output from the triplets generator has a shape of (3,5,279) which is 3 inputs(anchor,positive and negative) for 5 batches and a total of 279 features. When i run the fit_generator it throws this error that "the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 3 array(s), but instead got the following list of 1 arrays" meanwhile i have passed a list of three arrays. the code is below. it works when i use the fit, however, i want to always call the generator function to generate my triplets as my batches. thanks in advance..this has taken me three days
def load_data():
path = "arrhythmia_data.txt"
f = open( path, "r")
data = []
#remove line breaker, comma separate and store in array
for line in f:
line = line.replace('\n','').replace('?','0')
line = line.split(",")
data.append(line)
f.close()
data = np.array(data).astype(np.float64)
#print(data.shape)
#create the class labels for input data
Y_train = data[:,-1:]
train = data[:,:-1]
normaliser = preprocessing.MinMaxScaler()
train = normaliser.fit_transform(train)
val = train[320:,:]
train = train[:320,:]
#create one hot encoding of the class labels of the data and separate them into train and test data
lb = LabelBinarizer()
encode = lb.fit_transform(Y_train)
nb_classes = int(len(encode[0]))
#one_hot_labels = keras.utils.to_categorical(labels, num_classes=10) this could also be used for one hot encoding
Y_val_e = encode[320:,:]
Y_train_e = encode[:320,:]
print(Y_train_e[0])
print(np.argmax(Y_train_e[0]))
val_in = []
train_in = []
#grouping and sorting the input data based on label id or name
for n in range(nb_classes):
images_class_n = np.asarray([row for idx,row in enumerate(train) if np.argmax(Y_train_e[idx])==n])
train_in.append(images_class_n)
images_class_n = np.asarray([row for idx,row in enumerate(val) if np.argmax(Y_val_e[idx])==n])
val_in.append(images_class_n)
#print(train_in[0].shape)
return train_in,val_in,Y_train_e,Y_val_e,nb_classes
train_in,val,Y_train,Y_val,nb_classes = load_data()
input_shape = (train_in[0].shape[1],)
def build_network(input_shape , embeddingsize):
'''
Define the neural network to learn image similarity
Input :
input_shape : shape of input images
embeddingsize : vectorsize used to encode our picture
'''
#in_ = Input(train.shape)
net = Sequential()
net.add(Dense(128, activation='relu', input_shape=input_shape))
net.add(Dense(128, activation='relu'))
net.add(Dense(256, activation='relu'))
net.add(Dense(4096, activation='sigmoid'))
net.add(Dense(embeddingsize, activation= None))
#Force the encoding to live on the d-dimentional hypershpere
net.add(Lambda(lambda x: K.l2_normalize(x,axis=-1)))
return net
class TripletLossLayer(Layer):
def __init__(self, alpha, **kwargs):
self.alpha = alpha
super(TripletLossLayer, self).__init__(**kwargs)
def triplet_loss(self, inputs):
anchor, positive, negative = inputs
p_dist = K.sum(K.square(anchor-positive), axis=-1)
n_dist = K.sum(K.square(anchor-negative), axis=-1)
return K.sum(K.maximum(p_dist - n_dist + self.alpha, 0), axis=0)
def call(self, inputs):
loss = self.triplet_loss(inputs)
self.add_loss(loss)
return loss
def build_model(input_shape, network, margin=0.2):
'''
Define the Keras Model for training
Input :
input_shape : shape of input images
network : Neural network to train outputing embeddings
margin : minimal distance between Anchor-Positive and Anchor-Negative for the lossfunction (alpha)
'''
# Define the tensors for the three input images
anchor_input = Input(input_shape, name="anchor_input")
positive_input = Input(input_shape, name="positive_input")
negative_input = Input(input_shape, name="negative_input")
# Generate the encodings (feature vectors) for the three images
encoded_a = network(anchor_input)
encoded_p = network(positive_input)
encoded_n = network(negative_input)
#TripletLoss Layer
loss_layer = TripletLossLayer(alpha=margin,name='triplet_loss_layer')([encoded_a,encoded_p,encoded_n])
# Connect the inputs with the outputs
network_train = Model(inputs=[anchor_input,positive_input,negative_input],outputs=loss_layer)
# return the model
return network_train
def get_batch_random(batch_size,s="train"):
# initialize result
triplets=[np.zeros((batch_size,m)) for i in range(3)]
for i in range(batch_size):
#Pick one random class for anchor
anchor_class = np.random.randint(0, nb_classes)
nb_sample_available_for_class_AP = X[anchor_class].shape[0]
#Pick two different random pics for this class => A and P. You can use same anchor as P if there is one one element for anchor
if nb_sample_available_for_class_AP<=1:
continue
[idx_A,idx_P] = np.random.choice(nb_sample_available_for_class_AP,size=2 ,replace=False)
#Pick another class for N, different from anchor_class
negative_class = (anchor_class + np.random.randint(1,nb_classes)) % nb_classes
nb_sample_available_for_class_N = X[negative_class].shape[0]
#Pick a random pic for this negative class => N
idx_N = np.random.randint(0, nb_sample_available_for_class_N)
triplets[0][i,:] = X[anchor_class][idx_A,:]
triplets[1][i,:] = X[anchor_class][idx_P,:]
triplets[2][i,:] = X[negative_class][idx_N,:]
return np.array(triplets)
def get_batch_hard(draw_batch_size,hard_batchs_size,norm_batchs_size,network,s="train"):
if s == 'train':
X = train_in
else:
X = val
#m, features = X[0].shape
#while True:
#Step 1 : pick a random batch to study
studybatch = get_batch_random(draw_batch_size,X)
#Step 2 : compute the loss with current network : d(A,P)-d(A,N). The alpha parameter here is omited here since we want only to order them
studybatchloss = np.zeros((draw_batch_size))
#Compute embeddings for anchors, positive and negatives
A = network.predict(studybatch[0])
P = network.predict(studybatch[1])
N = network.predict(studybatch[2])
#Compute d(A,P)-d(A,N)
studybatchloss = np.sum(np.square(A-P),axis=1) - np.sum(np.square(A-N),axis=1)
#Sort by distance (high distance first) and take the
selection = np.argsort(studybatchloss)[::-1][:hard_batchs_size]
#Draw other random samples from the batch
selection2 = np.random.choice(np.delete(np.arange(draw_batch_size),selection),norm_batchs_size,replace=False)
selection = np.append(selection,selection2)
triplets = [studybatch[0][selection,:], studybatch[1][selection,:],studybatch[2][selection,:]]
triplets = triplets.reshape(triplets.shape[0],triplets.shape[1],triplets.shape[2])
yield triplets
network = build_network(input_shape,embeddingsize=10)
hard = get_batch_hard(5,4,1,network,s="train")
network_train = build_model(input_shape,network)
optimizer = Adam(lr = 0.00006)
network_train.compile(loss=None,optimizer=optimizer)
#this works
#history = network_train.fit(hard,epochs=100,steps_per_epoch=1, verbose=2)
history = network_train.fit_generator(hard,epochs=10,steps_per_epoch=16, verbose=2)
# error:: the list of Numpy arrays that you are passing to your model is not the size the model
expected. Expected to see 3 array(s), but instead got the following list of 1 arrays:
I think that's beacause in your generator you are yielding the 3 inputs array in one list, you need to yield the 3 arrays independently:
triplet_1 = studybatch[0][selection,:]
triplet_2 = studybatch[1][selection,:]
triplet_3 = studybatch[2][selection,:]
yield [triplet_1, triplet_2, triplet_3]
I am dealing with an issue in my conversion over to the Dataset API and I guess I just don't have enough experience yet with the API to know how to handle the below situation. We currently have image augmentation that we perform currently using queueing and batching. I was tasked with checking out the new Dataset API and converting over our existing implementation using it rather than queues.
What we would like to do is get a reference to all the paths and handle all operations from just that reference. As you see in the dataset initialization, I have mapped the parse_fn to the dataset itself which then goes about reading the file and extracting the initial values from the filenames. However when I then go about calling the iterators next_batch method and then pass those values to get_summary, I'm now getting an error around shape. I have been trying a number of things which just keeps changing the error and so I felt I should see if anyone on SO saw possibly that I was going about this all wrong and should be taking a different route. Does anything jump out as absolutely wrong in my use of the Dataset API?
Should I not be calling the ops this way any longer? I noticed the majority of the examples I saw they would get the batch, pass the variables to the op and then capture that in a variable and pass that to sess.run, however I haven't found an easy way of doing that as of yet with our setup that wasn't erroring so this was the approach I took instead (but its still erroring). I'll be continuing to try to trace down the problem and post here should I find anything, but if anyone sees something please advise. Thanks!
Current Error:
... in get_summary summary, acc = sess.run([self._summary_op,
self._accuracy], feed_dict=feed_dict) ValueError: Cannot feed value of
shape (32,) for Tensor 'ph_input_labels:0', which has shape '(?, 1)
Below is the block where the get_summary method is called and error is fired:
def perform_train():
if __name__ == '__main__':
#Get all our image paths
filenames = data_layer_train.get_image_paths()
next_batch, iterator = preproc_image_fn(filenames=filenames)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
with sess.graph.as_default():
# Set the random seed for tensorflow
tf.set_random_seed(cfg.RNG_SEED)
classifier_network = c_common.create_model(len(products_to_class_dict), is_training=True)
optimizer, global_step_var = c_common.create_optimizer(classifier_network)
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
# Init tables and dataset iterator
sess.run(tf.tables_initializer())
sess.run(iterator.initializer)
cur_epoch = 0
blobs = None
try:
epoch_size = data_layer_train.get_steps_per_epoch()
num_steps = num_epochs * epoch_size
for step in range(num_steps):
timer_summary.tic()
if blobs is None:
#Now populate from our training dataset
blobs = sess.run(next_batch)
# *************** Below is where it is erroring *****************
summary_train, acc = classifier_network.get_summary(sess, blobs["images"], blobs["labels"], blobs["weights"])
...
Believe the error is in preproc_image_fn:
def preproc_image_fn(filenames, images=None, labels=None, image_paths=None, cells=None, weights=None):
def _parse_fn(filename, label, weight):
augment_instance = False
paths=[]
selected_cells=[]
if vals.FIRST_ITER:
#Perform our check of the path to see if _data_augmentation is within it
#If so set augment_instance to true and replace the substring with an empty string
new_filename = tf.regex_replace(filename, "_data_augmentation", "")
contains = tf.equal(tf.size(tf.string_split([filename], "")), tf.size(tf.string_split([new_filename])))
filename = new_filename
if contains is True:
augment_instance = True
core_file = tf.string_split([filename], '\\').values[-1]
product_id = tf.string_split([core_file], ".").values[0]
label = search_tf_table_for_entry(product_id)
weight = data_layer_train.get_weights(product_id)
image_string = tf.read_file(filename)
img = tf.image.decode_image(image_string, channels=data_layer_train._channels)
img.set_shape([None, None, None])
img = tf.image.resize_images(img, [data_layer_train._target_height, data_layer_train._target_width])
#Previously I was returning the below, but I was getting an error from the op when assigning feed_dict stating that it didnt like the dictionary
#retval = dict(zip([filename], [img])), label, weight
retval = img, label, weight
return retval
num_files = len(filenames)
filenames = tf.constant(filenames)
#*********** Setup dataset below ************
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels, weights))
dataset=dataset.map(_parse_fn)
dataset = dataset.repeat()
dataset = dataset.batch(32)
iterator = dataset.make_initializable_iterator()
batch_features, batch_labels, batch_weights = iterator.get_next()
return {'images': batch_features, 'labels': batch_labels, 'weights': batch_weights}, iterator
def search_tf_table_for_entry(self, product_id):
'''Looks up keys in the table and outputs the values. Will return -1 if not found '''
if product_id is not None:
return self._products_to_class_table.lookup(product_id)
else:
if not self._real_eval:
logger().info("class not found in training {} ".format(product_id))
return -1
Where I create the model and have the placeholders used previously:
...
def create_model(self):
weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY)
biases_regularizer = weights_regularizer
# Input data.
self._input_images = tf.placeholder(
tf.float32, shape=(None, self._image_height, self._image_width, self._num_channels), name="ph_input_images")
self._input_labels = tf.placeholder(tf.int64, shape=(None, 1), name="ph_input_labels")
self._input_weights = tf.placeholder(tf.float32, shape=(None, 1), name="ph_input_weights")
self._is_training = tf.placeholder(tf.bool, name='ph_is_training')
self._keep_prob = tf.placeholder(tf.float32, name="ph_keep_prob")
self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, tf.float32))
...
self.create_summaries()
def create_summaries(self):
val_summaries = []
with tf.device("/cpu:0"):
for var in self._act_summaries:
self._add_act_summary(var)
for var in self._train_summaries:
self._add_train_summary(var)
self._summary_op = tf.summary.merge_all()
self._summary_op_val = tf.summary.merge(val_summaries)
def get_summary(self, sess, images, labels, weights):
feed_dict = {self._input_images: images, self._input_labels: labels,
self._input_weights: weights, self._is_training: False}
summary, acc = sess.run([self._summary_op, self._accuracy], feed_dict=feed_dict)
return summary, acc
Since the error says:
Cannot feed value of shape (32,) for Tensor 'ph_input_labels:0', which has shape '(?, 1)
My guess is your labels in get_summary has the shape [32]. Can you just reshape it to (32, 1)? Or maybe reshape the label earlier in _parse_fn?
For the reinforcement learning one usually applies forward pass of the neural network for each step of the episode in order to calculate policy. Afterwards one could calculate parameter gradients using backpropagation. Simplified implementation of my network looks like this:
class AC_Network(object):
def __init__(self, s_size, a_size, scope, trainer, parameters_net):
with tf.variable_scope(scope):
self.is_training = tf.placeholder(shape=[], dtype=tf.bool)
self.inputs = tf.placeholder(shape=[None, s_size], dtype=tf.float32)
# (...)
layer = slim.fully_connected(self.inputs,
layer_size,
activation_fn=tf.nn.relu,
biases_initializer=None)
layer = tf.contrib.layers.dropout(inputs=layer, keep_prob=parameters_net["dropout_keep_prob"],
is_training=self.is_training)
self.policy = slim.fully_connected(layer, a_size,
activation_fn=tf.nn.softmax,
biases_initializer=None)
self.actions = tf.placeholder(shape=[None], dtype=tf.int32)
self.advantages = tf.placeholder(shape=[None], dtype=tf.float32)
actions_onehot = tf.one_hot(self.actions, a_size, dtype=tf.float32)
responsible_outputs = tf.reduce_sum(self.policy * actions_onehot, [1])
self.policy_loss = - policy_loss_multiplier * tf.reduce_mean(tf.log(responsible_outputs) * self.advantages)
local_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
self.gradients = tf.gradients(self.policy_loss, local_vars)
Now during training I will fist rollout the episode by consecutive forward passes (again, simplified version):
s = self.local_env.reset() # list of input variables for the first step
while done == False:
a_dist = sess.run([self.policy],
feed_dict = {self.local_AC.inputs: [s],
self.is_training: True})
a = np.argmax(a_dist)
s, r, done, extra_stat = self.local_env.step(a)
# (...)
and in the end I will calculate gradients by backward pass:
p_l, grad = sess.run([self.policy_loss,
self.gradients],
feed_dict={self.inputs: np.vstack(comb_observations),
self.is_training: True,
self.actions: np.hstack(comb_actions),})
(please note that I could have made a mistake somewhere above trying to remove as much as possible of the original code irrelevant to the issue in question)
So finally the question: Is there a way of ensuring that all the consecutive calls to the sess.run() will generate the same dropout structure? Ideally I would like to have exactly the same dropout structure within each episode and only change it between episodes. Things seem to work well as they are but I continue to wonder.