Display values of a dictionary in Tensorlow
Hello i'm new to TensorFlow and i'm trying to create a dictionary of weigths per layer for my ANN implementation.
The issue is that although i create the dictionary with strings as keys and tensors as values i don't know how to display them when i call the init_weight method
def init_weights(topology):
#topology: dimensions of the network
for i in range(1,len(topology)):
parameters['W' + str(i)] = tf.Variable(tf.random_normal([topology[i-1],topology[i]]))
the output of the method shows the following:
{'W1': <tf.Variable 'Variable_1:0' shape=(2, 5) dtype=float32_ref>,
'W2': <tf.Variable 'Variable_3:0' shape=(5, 5) dtype=float32_ref>,
'W3': <tf.Variable 'Variable_5:0' shape=(5, 5) dtype=float32_ref>,
'W4': <tf.Variable 'Variable_7:0' shape=(5, 10) dtype=float32_ref>}
My question is how can print the weights matrices?
python dictionary tensorflow neural-network weight
asked Nov 16 '18 at 0:22
max thundermax thunder
51
add a comment |
Hello i'm new to TensorFlow and i'm trying to create a dictionary of weigths per layer for my ANN implementation.
The issue is that although i create the dictionary with strings as keys and tensors as values i don't know how to display them when i call the init_weight method
def init_weights(topology):
#topology: dimensions of the network
for i in range(1,len(topology)):
parameters['W' + str(i)] = tf.Variable(tf.random_normal([topology[i-1],topology[i]]))
the output of the method shows the following:
{'W1': <tf.Variable 'Variable_1:0' shape=(2, 5) dtype=float32_ref>,
'W2': <tf.Variable 'Variable_3:0' shape=(5, 5) dtype=float32_ref>,
'W3': <tf.Variable 'Variable_5:0' shape=(5, 5) dtype=float32_ref>,
'W4': <tf.Variable 'Variable_7:0' shape=(5, 10) dtype=float32_ref>}
My question is how can print the weights matrices?
python dictionary tensorflow neural-network weight
asked Nov 16 '18 at 0:22
max thundermax thunder
51
add a comment |
Hello i'm new to TensorFlow and i'm trying to create a dictionary of weigths per layer for my ANN implementation.
The issue is that although i create the dictionary with strings as keys and tensors as values i don't know how to display them when i call the init_weight method
def init_weights(topology):
#topology: dimensions of the network
for i in range(1,len(topology)):
parameters['W' + str(i)] = tf.Variable(tf.random_normal([topology[i-1],topology[i]]))
the output of the method shows the following:
{'W1': <tf.Variable 'Variable_1:0' shape=(2, 5) dtype=float32_ref>,
'W2': <tf.Variable 'Variable_3:0' shape=(5, 5) dtype=float32_ref>,
'W3': <tf.Variable 'Variable_5:0' shape=(5, 5) dtype=float32_ref>,
'W4': <tf.Variable 'Variable_7:0' shape=(5, 10) dtype=float32_ref>}
My question is how can print the weights matrices?
python dictionary tensorflow neural-network weight
asked Nov 16 '18 at 0:22
max thundermax thunder
51
Hello i'm new to TensorFlow and i'm trying to create a dictionary of weigths per layer for my ANN implementation.
The issue is that although i create the dictionary with strings as keys and tensors as values i don't know how to display them when i call the init_weight method
def init_weights(topology):
#topology: dimensions of the network
for i in range(1,len(topology)):
parameters['W' + str(i)] = tf.Variable(tf.random_normal([topology[i-1],topology[i]]))
the output of the method shows the following:
{'W1': <tf.Variable 'Variable_1:0' shape=(2, 5) dtype=float32_ref>,
'W2': <tf.Variable 'Variable_3:0' shape=(5, 5) dtype=float32_ref>,
'W3': <tf.Variable 'Variable_5:0' shape=(5, 5) dtype=float32_ref>,
'W4': <tf.Variable 'Variable_7:0' shape=(5, 10) dtype=float32_ref>}
My question is how can print the weights matrices?
python dictionary tensorflow neural-network weight
python dictionary tensorflow neural-network weight
asked Nov 16 '18 at 0:22
max thundermax thunder
51
asked Nov 16 '18 at 0:22
max thundermax thunder
51
asked Nov 16 '18 at 0:22
max thundermax thunder
51
asked Nov 16 '18 at 0:22
max thundermax thunder
51
asked Nov 16 '18 at 0:22
max thundermax thunder
51
51
add a comment |
add a comment |
1 Answer
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Tensorflow is a statically typed framework (guess this is changing in 2.0). Meaning, you first build a static graph and the graph has values only when run using Tf.Session(). Now to answer your question. There are two ways to get what you want.
Add
tf.enable_eager_execution()at the start of your script. This creates a dynamic graph (similar to Pytorch). Your same code without any extra addition will give you what you want.Wrap everything into a
tf.Session()and run it. You will get the weight matrices
answered Nov 16 '18 at 0:31
Abhijit BalajiAbhijit Balaji
545421
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Tensorflow is a statically typed framework (guess this is changing in 2.0). Meaning, you first build a static graph and the graph has values only when run using Tf.Session(). Now to answer your question. There are two ways to get what you want.
Add
tf.enable_eager_execution()at the start of your script. This creates a dynamic graph (similar to Pytorch). Your same code without any extra addition will give you what you want.Wrap everything into a
tf.Session()and run it. You will get the weight matrices
answered Nov 16 '18 at 0:31
Abhijit BalajiAbhijit Balaji
545421
add a comment |
Tensorflow is a statically typed framework (guess this is changing in 2.0). Meaning, you first build a static graph and the graph has values only when run using Tf.Session(). Now to answer your question. There are two ways to get what you want.
Add
tf.enable_eager_execution()at the start of your script. This creates a dynamic graph (similar to Pytorch). Your same code without any extra addition will give you what you want.Wrap everything into a
tf.Session()and run it. You will get the weight matrices
answered Nov 16 '18 at 0:31
Abhijit BalajiAbhijit Balaji
545421
add a comment |
Tensorflow is a statically typed framework (guess this is changing in 2.0). Meaning, you first build a static graph and the graph has values only when run using Tf.Session(). Now to answer your question. There are two ways to get what you want.
Add
tf.enable_eager_execution()at the start of your script. This creates a dynamic graph (similar to Pytorch). Your same code without any extra addition will give you what you want.Wrap everything into a
tf.Session()and run it. You will get the weight matrices
answered Nov 16 '18 at 0:31
Abhijit BalajiAbhijit Balaji
545421
Tensorflow is a statically typed framework (guess this is changing in 2.0). Meaning, you first build a static graph and the graph has values only when run using Tf.Session(). Now to answer your question. There are two ways to get what you want.
Add
tf.enable_eager_execution()at the start of your script. This creates a dynamic graph (similar to Pytorch). Your same code without any extra addition will give you what you want.Wrap everything into a
tf.Session()and run it. You will get the weight matrices
answered Nov 16 '18 at 0:31
Abhijit BalajiAbhijit Balaji
545421
answered Nov 16 '18 at 0:31
Abhijit BalajiAbhijit Balaji
545421
answered Nov 16 '18 at 0:31
Abhijit BalajiAbhijit Balaji
545421
answered Nov 16 '18 at 0:31
Abhijit BalajiAbhijit Balaji
545421
545421
add a comment |
add a comment |
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