I'd like to apply the pagerank algorithm to the x_attn tensor. But the nx.pagerank module only accepts numpy arrays. When I try to convert it to using x_att.eval() , it says:
"tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'main_input_5' with dtype float and shape [?,6600]".
Can somebody please help me out?
def variable_attn_15jan():
input_dim=input_dim_func()
main_input = Input(shape=(input_dim,),name='main_input')
inputs_w1=Lambda(lambda x: x[:,0:3300])(main_input)
inputs_w2=Lambda(lambda x: x[:,3300:6600])(main_input)
x1_attn= Dense(11, activation='softmax')(inputs_w1)
x2_attn= Dense(11, activation='softmax')(inputs_w2)
list_x_att1=[]
list_x_att2=[]
for i in range(11) :
val_scalar=Lambda(lambda x: x[:,i:(i+1)])(x1_attn)
list_x_att1.append(Lambda(lambda x: x[:,(i*300):(i+1)*300]*val_scalar)(inputs_w1))
x_att1 = concatenate(list_x_att1)
for i in range(11) :
val_scalar=Lambda(lambda x: x[:,i:(i+1)])(x2_attn)
list_x_att2.append(Lambda(lambda x: x[:,(i*300):(i+1)*300]*val_scalar)(inputs_w2))
x_att2 = concatenate(list_x_att2)
x_att = concatenate([x_att1,x_att2])
On your tensor (v2.0):
npa = tf.numpy()
where npa will be your numpy array name.
Alternatively, tensor (< v2.0):
npa=tf.eval()
print(type(npa))
Update 1:
Use below code how to check what type of arrays you've got. Look also at comment from rkern commented posted here on Jul 14, 2019 .
type(tf)
type(np.array(tf))
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