error: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,3)
0
an error appears when running my machine learning code.
i have just started exploring neural networks and machine learning but i don't know why this is happening or what it means.
for iteration in range(20000):
input_layer = training_inputs
outputs = sigmoid(np.dot(input_layer, synaptic_weights))
error = training_inputs - outputs
adjustment = error * sigmoid_derivative(outputs)
synaptic_weights += np.dot(input_layer.T, adjustment)#error occurs here
*edit:
this is entire code
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
training_inputs = np.array([[0,0,1],
[1,1,1],
[1,0,1],
[0,1,1]])
def sigmoid_derivative(x):
return x * (1-x)
training_outputs = np.array([[0,1,1,0]]).T
np.random.seed(1)
synaptic_weights = 2 * np.random.random((3, 1)) - 1
print ('random starting syanptic weights: ')
print (synaptic_weights)
for iteration in range(1):
input_layer = training_inputs
outputs = sigmoid(np.dot(input_layer, synaptic_weights))
error = training_inputs - outputs
adjustment = error * sigmoid_derivative(outputs)
synaptic_weights += np.dot(input_layer.T, adjustment)
print(' synaptic weights after training: ')
print (synaptic_weights)
print ('outputs after training: ')
print (outputs)
python numpy neural-network
asked Jan 20 at 7:00
BOBTHEBUILDERBOBTHEBUILDER
5910
add a comment |
0
an error appears when running my machine learning code.
i have just started exploring neural networks and machine learning but i don't know why this is happening or what it means.
for iteration in range(20000):
input_layer = training_inputs
outputs = sigmoid(np.dot(input_layer, synaptic_weights))
error = training_inputs - outputs
adjustment = error * sigmoid_derivative(outputs)
synaptic_weights += np.dot(input_layer.T, adjustment)#error occurs here
*edit:
this is entire code
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
training_inputs = np.array([[0,0,1],
[1,1,1],
[1,0,1],
[0,1,1]])
def sigmoid_derivative(x):
return x * (1-x)
training_outputs = np.array([[0,1,1,0]]).T
np.random.seed(1)
synaptic_weights = 2 * np.random.random((3, 1)) - 1
print ('random starting syanptic weights: ')
print (synaptic_weights)
for iteration in range(1):
input_layer = training_inputs
outputs = sigmoid(np.dot(input_layer, synaptic_weights))
error = training_inputs - outputs
adjustment = error * sigmoid_derivative(outputs)
synaptic_weights += np.dot(input_layer.T, adjustment)
print(' synaptic weights after training: ')
print (synaptic_weights)
print ('outputs after training: ')
print (outputs)
python numpy neural-network
asked Jan 20 at 7:00
BOBTHEBUILDERBOBTHEBUILDER
5910
With 2d arrays,np.dotexpects the last dimension of the first to match the 2nd to the last dimension of the second (the pairing of rows and columns of a matrix product). Like a good BUILDER, check the shape frequently - you know the routine - measure twice, cut once.
– hpaulj
Jan 20 at 7:52
@hpaulj so how do i fix it? (i'm a noob)
– BOBTHEBUILDER
Jan 20 at 7:59
We don't know what you are trying to do, and we don't know the shape(s) of the various arrays in your problem (other than the ones that gave the error).
– hpaulj
Jan 20 at 8:03
i'll edit it @hpaulj
– BOBTHEBUILDER
Jan 20 at 10:37
add a comment |
0
0
0
an error appears when running my machine learning code.
i have just started exploring neural networks and machine learning but i don't know why this is happening or what it means.
for iteration in range(20000):
input_layer = training_inputs
outputs = sigmoid(np.dot(input_layer, synaptic_weights))
error = training_inputs - outputs
adjustment = error * sigmoid_derivative(outputs)
synaptic_weights += np.dot(input_layer.T, adjustment)#error occurs here
*edit:
this is entire code
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
training_inputs = np.array([[0,0,1],
[1,1,1],
[1,0,1],
[0,1,1]])
def sigmoid_derivative(x):
return x * (1-x)
training_outputs = np.array([[0,1,1,0]]).T
np.random.seed(1)
synaptic_weights = 2 * np.random.random((3, 1)) - 1
print ('random starting syanptic weights: ')
print (synaptic_weights)
for iteration in range(1):
input_layer = training_inputs
outputs = sigmoid(np.dot(input_layer, synaptic_weights))
error = training_inputs - outputs
adjustment = error * sigmoid_derivative(outputs)
synaptic_weights += np.dot(input_layer.T, adjustment)
print(' synaptic weights after training: ')
print (synaptic_weights)
print ('outputs after training: ')
print (outputs)
python numpy neural-network
asked Jan 20 at 7:00
BOBTHEBUILDERBOBTHEBUILDER
5910
an error appears when running my machine learning code.
i have just started exploring neural networks and machine learning but i don't know why this is happening or what it means.
for iteration in range(20000):
input_layer = training_inputs
outputs = sigmoid(np.dot(input_layer, synaptic_weights))
error = training_inputs - outputs
adjustment = error * sigmoid_derivative(outputs)
synaptic_weights += np.dot(input_layer.T, adjustment)#error occurs here
*edit:
this is entire code
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
training_inputs = np.array([[0,0,1],
[1,1,1],
[1,0,1],
[0,1,1]])
def sigmoid_derivative(x):
return x * (1-x)
training_outputs = np.array([[0,1,1,0]]).T
np.random.seed(1)
synaptic_weights = 2 * np.random.random((3, 1)) - 1
print ('random starting syanptic weights: ')
print (synaptic_weights)
for iteration in range(1):
input_layer = training_inputs
outputs = sigmoid(np.dot(input_layer, synaptic_weights))
error = training_inputs - outputs
adjustment = error * sigmoid_derivative(outputs)
synaptic_weights += np.dot(input_layer.T, adjustment)
print(' synaptic weights after training: ')
print (synaptic_weights)
print ('outputs after training: ')
print (outputs)
python numpy neural-network
python numpy neural-network
asked Jan 20 at 7:00
BOBTHEBUILDERBOBTHEBUILDER
5910
asked Jan 20 at 7:00
BOBTHEBUILDERBOBTHEBUILDER
5910
edited Jan 20 at 10:40
BOBTHEBUILDER
asked Jan 20 at 7:00
BOBTHEBUILDERBOBTHEBUILDER
5910
asked Jan 20 at 7:00
BOBTHEBUILDERBOBTHEBUILDER
5910
asked Jan 20 at 7:00
BOBTHEBUILDERBOBTHEBUILDER
5910
5910
With 2d arrays,np.dotexpects the last dimension of the first to match the 2nd to the last dimension of the second (the pairing of rows and columns of a matrix product). Like a good BUILDER, check the shape frequently - you know the routine - measure twice, cut once.
– hpaulj
Jan 20 at 7:52
@hpaulj so how do i fix it? (i'm a noob)
– BOBTHEBUILDER
Jan 20 at 7:59
We don't know what you are trying to do, and we don't know the shape(s) of the various arrays in your problem (other than the ones that gave the error).
– hpaulj
Jan 20 at 8:03
i'll edit it @hpaulj
– BOBTHEBUILDER
Jan 20 at 10:37
add a comment |
With 2d arrays,np.dotexpects the last dimension of the first to match the 2nd to the last dimension of the second (the pairing of rows and columns of a matrix product). Like a good BUILDER, check the shape frequently - you know the routine - measure twice, cut once.
– hpaulj
Jan 20 at 7:52
@hpaulj so how do i fix it? (i'm a noob)
– BOBTHEBUILDER
Jan 20 at 7:59
We don't know what you are trying to do, and we don't know the shape(s) of the various arrays in your problem (other than the ones that gave the error).
– hpaulj
Jan 20 at 8:03
i'll edit it @hpaulj
– BOBTHEBUILDER
Jan 20 at 10:37
With 2d arrays,
np.dot expects the last dimension of the first to match the 2nd to the last dimension of the second (the pairing of rows and columns of a matrix product). Like a good BUILDER, check the shape frequently - you know the routine - measure twice, cut once.– hpaulj
Jan 20 at 7:52
With 2d arrays,
np.dot expects the last dimension of the first to match the 2nd to the last dimension of the second (the pairing of rows and columns of a matrix product). Like a good BUILDER, check the shape frequently - you know the routine - measure twice, cut once.– hpaulj
Jan 20 at 7:52
@hpaulj so how do i fix it? (i'm a noob)
– BOBTHEBUILDER
Jan 20 at 7:59
@hpaulj so how do i fix it? (i'm a noob)
– BOBTHEBUILDER
Jan 20 at 7:59
We don't know what you are trying to do, and we don't know the shape(s) of the various arrays in your problem (other than the ones that gave the error).
– hpaulj
Jan 20 at 8:03
We don't know what you are trying to do, and we don't know the shape(s) of the various arrays in your problem (other than the ones that gave the error).
– hpaulj
Jan 20 at 8:03
i'll edit it @hpaulj
– BOBTHEBUILDER
Jan 20 at 10:37
i'll edit it @hpaulj
– BOBTHEBUILDER
Jan 20 at 10:37
add a comment |
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With 2d arrays,
np.dotexpects the last dimension of the first to match the 2nd to the last dimension of the second (the pairing of rows and columns of a matrix product). Like a good BUILDER, check the shape frequently - you know the routine - measure twice, cut once.– hpaulj
Jan 20 at 7:52
@hpaulj so how do i fix it? (i'm a noob)
– BOBTHEBUILDER
Jan 20 at 7:59
We don't know what you are trying to do, and we don't know the shape(s) of the various arrays in your problem (other than the ones that gave the error).
– hpaulj
Jan 20 at 8:03
i'll edit it @hpaulj
– BOBTHEBUILDER
Jan 20 at 10:37