def main():
input = np.array([0.5, 2])
# w1 = np.array([[1,2,3], [3,4,5]])
w1 = np.array([[1,2,3], [5,4,3]])
w2 = np.array([3,2,1])
np.array([[1,2,3],[4,5,6]]).dot(np.array([[1],[2],[3]]))
np.array([[1,2,3],[4,5,6]]).dot(np.array([1,2,3]))
np.prod(np.array([1,2,3]), np.array([4,5,6]))
np.prod([2,3])
np.prod(np.array([2,3,8]))
tf.prod([1,2,3])
return (input.dot(w1) + 1).dot(w2)+1
def batch_segment_mean(s_data, s_indices, n):
s_data_shp = tf.shape(s_data)
s_data_flat = tf.reshape(s_data,
[tf.prod(s_data_shp[:-1]), s_data_shp[-1]])
s_indices_flat = tf.reshape(s_indices, [-1])
s_results = tf.unsorted_segment_sum(s_data_flat, s_indices_flat, n)
s_weights = tf.unsorted_segment_sum(tf.ones_like(s_indices_flat),
s_indices_flat, n)
return s_results / tf.cast(tf.expand_dims(s_weights, -1), hparams.FLOATX)
def reshape(tensor, dims_ilfst):
shape = getshape(tensor)
dims_prod = []
for dims in dims_list:
if isinstance(dims, int):
dims_prod.append(shape[dims])
elif all([isinstance(shape[d], int)] for d in dims):
dims_prod.append(np.prod([shape[d] for d in dims]))
else:
dims_prod.append(tf.prod([shape[d] for d in dims]))
tensor = tf.reshape(tensor, dims_prod)
return tensor
def reshape(tensor, dims_list):
"""Reshape the given tensor by collapsing dimensions."""
shape = get_shape(tensor)
dims_prod = []
for dims in dims_list:
if isinstance(dims, int):
dims_prod.append(shape[dims])
elif all([isinstance(shape[d], int) for d in dims]):
dims_prod.append(np.prod([shape[d] for d in dims]))
else:
dims_prod.append(tf.prod([shape[d] for d in dims]))
tensor = tf.reshape(tensor, dims_prod)
return tensor
def reshape(tensor, dims_list):
"""Reshape the given tensor by collapsing dimensions."""
shape = get_shape(tensor)
dims_prod = []
for dims in dims_list:
if isinstance(dims, int):
dims_prod.append(shape[dims])
elif all([isinstance(shape[d], int) for d in dims]):
dims_prod.append(np.prod([shape[d] for d in dims]))
else:
dims_prod.append(tf.prod([shape[d] for d in dims]))
tensor = tf.reshape(tensor, dims_prod)
return tensor
def _num_toks(y):
return tf.prod(get_shape_as_list(y))