tensorrec
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jfkirk
Error on dimensionality
Hi, I am facing a problem with dimensionality when I build the model using DeepRepresentationGraph as per your example
Specifically
class DeepRepresentationGraph(AbstractKerasRepresentationGraph):
# This method returns an ordered list of Keras layers connecting the user/item features to the user/item
# representation. When TensorRec learns, the learning will happen in these layers.
def create_layers(self, n_features, n_components):
return [
ks.layers.Dense(n_components * 16, activation='relu'), #rectified linear unit
ks.layers.Dense(n_components * 8, activation='relu'), #you can try other activation layer too
ks.layers.Dense(n_components * 2, activation='relu'), #most likely changes only benefit other ML like image recog.
ks.layers.Dense(n_components, activation='tanh'),
]
n_sampled_items = int(item_features.shape[0] * .1)
model = TensorRec(n_components=n_components,
user_repr_graph=DeepRepresentationGraph(),
item_repr_graph=NormalizedLinearRepresentationGraph(),
loss_graph=WMRBLossGraph(),
biased=biased)
model.fit(train_interactions,
user_features,
item_features,
epochs=epochs,
verbose=False,
alpha=alpha,
n_sampled_items=n_sampled_items,
learning_rate=learning_rate)
Please bear in mind that train_interactions, user_features and item_features are all scipy.sparse.coo_matrix
The error I get is the following
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\_pydevd_bundle\pydevd_exec2.py", line 3, in Exec
exec(exp, global_vars, local_vars)
File "<input>", line 1, in <module>
File "C:\Users\xyzxyz\PycharmProjects\recommender_tf\model.py", line 47, in fit
learning_rate=learning_rate)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorrec\tensorrec.py", line 538, in fit
n_sampled_items=n_sampled_items)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorrec\tensorrec.py", line 623, in fit_partial
session.run(self.tf_optimizer, feed_dict=feed_dict)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\client\session.py", line 956, in run
run_metadata_ptr)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\client\session.py", line 1180, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\client\session.py", line 1359, in _do_run
run_metadata)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\client\session.py", line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[1107898] = 25196 is not in [0, 25196)
[[node GatherV2_6 (defined at C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\framework\ops.py:1748) ]]
Original stack trace for 'GatherV2_6':
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\pydevd.py", line 2127, in <module>
main()
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\pydevd.py", line 2118, in main
globals = debugger.run(setup['file'], None, None, is_module)
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\pydevd.py", line 1427, in run
return self._exec(is_module, entry_point_fn, module_name, file, globals, locals)
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\pydevd.py", line 1434, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/xyzxyz/PycharmProjects/recommender_tf/main.py", line 62, in <module>
main()
File "C:/Users/xyzxyz/PycharmProjects/recommender_tf/main.py", line 57, in main
preds = fit(item_f, user_f, train_interacions)
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\pydevd.py", line 1099, in do_wait_suspend
self._do_wait_suspend(thread, frame, event, arg, suspend_type, from_this_thread)
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\pydevd.py", line 1113, in _do_wait_suspend
self.process_internal_commands()
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\pydevd.py", line 818, in process_internal_commands
int_cmd.do_it(self)
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\_pydevd_bundle\pydevd_comm.py", line 1647, in do_it
result = pydevd_console_integration.console_exec(self.thread_id, self.frame_id, self.expression, dbg)
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\_pydevd_bundle\pydevd_console_integration.py", line 222, in console_exec
Exec(code, updated_globals, updated_globals)
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\_pydevd_bundle\pydevd_exec2.py", line 3, in Exec
exec(exp, global_vars, local_vars)
File "<input>", line 1, in <module>
File "C:\Users\xyzxyz\PycharmProjects\recommender_tf\model.py", line 47, in fit
learning_rate=learning_rate)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorrec\tensorrec.py", line 538, in fit
n_sampled_items=n_sampled_items)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorrec\tensorrec.py", line 606, in fit_partial
self._build_tf_graph(n_user_features=n_user_features, n_item_features=n_item_features)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorrec\tensorrec.py", line 389, in _build_tf_graph
tf_x_item=tf_x_item,
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorrec\prediction_graphs.py", line 53, in connect_serial_prediction_graph
gathered_user_reprs = tf.gather(tf_user_representation, tf_x_user)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\util\dispatch.py", line 180, in wrapper
return target(*args, **kwargs)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\ops\array_ops.py", line 3956, in gather
params, indices, axis, name=name)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\ops\gen_array_ops.py", line 4082, in gather_v2
batch_dims=batch_dims, name=name)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\framework\op_def_library.py", line 794, in _apply_op_helper
op_def=op_def)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\framework\ops.py", line 3357, in create_op
attrs, op_def, compute_device)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\framework\ops.py", line 3426, in _create_op_internal
op_def=op_def)
File "C:\Users\xyzxyz\anaconda3\envs\recommender_tf\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1748, in __init__
self._traceback = tf_stack.extract_stack()
I am using TF 1.15 and tensorrec 0.26.2
import tensorflow as tf
import numpy as np
from tensorrec import TensorRec
from tensorrec.representation_graphs import AbstractKerasRepresentationGraph
from tensorrec.loss_graphs import WMRBLossGraph
from tensorrec.prediction_graphs import NormalizedLinearRepresentationGraph
from scipy import sparse
# Define custom representation graph
class DeepRepresentationGraph(AbstractKerasRepresentationGraph):
def create_layers(self, n_features, n_components):
return [
tf.keras.layers.Dense(n_components * 16, activation='relu'),
tf.keras.layers.Dense(n_components * 8, activation='relu'),
tf.keras.layers.Dense(n_components * 2, activation='relu'),
tf.keras.layers.Dense(n_components, activation='tanh'),
]
# Sample data
n_users = 1000
n_items = 150
n_components = 10
train_interactions = sparse.random(n_users, n_items, density=0.1, format='coo')
# User and item features
user_features = sparse.random(n_users, n_components, density=0.1, format='csr')
item_features = sparse.random(n_items, n_components, density=0.1, format='csr')
# Define model parameters
n_sampled_items = int(item_features.shape[0] * .1)
biased = False
epochs = 10
alpha = 0
learning_rate = 0.01
# Build model
model = TensorRec(
n_components=n_components,
user_repr_graph=DeepRepresentationGraph(),
item_repr_graph=NormalizedLinearRepresentationGraph(),
loss_graph=WMRBLossGraph(),
biased=biased
)
# Fit model
model.fit(
train_interactions,
user_features,
item_features,
epochs=epochs,
verbose=False,
alpha=alpha,
n_sampled_items=n_sampled_items,
learning_rate=learning_rate
)
