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pythontensorflow
A Das
A Das

Reputation: 847

Does tensorflow map_fn support taking more than one tensor?

Does tf.map_fn support taking more than one tensors as is supported by python's native map function (example provided below)?

a = [1,2,3,4]
b = [17,12,11,10]
print(map(lambda x,y:x+y, a,b)) # ==> [18, 14, 14, 14]

Upvotes: 18

Views: 13881

Answers (5)

Avi Avidan
Avi Avidan

Reputation: 1096

you can combine approaches described in this page to pass a number of tensors and arguments to be considered when calling to your function, for example - 

import tensorflow as tf
cnn = tf.keras.layers.Conv1D(name, filters=64, kernel_size=4, padding='same')
pool = tf.keras.layers.GlobalAveragePooling1D()

def stack_inputs(inp1, inp2, axis=1):
    return tf.stack([inp1, inp2], axis)

def attention_op(q, p, cnn):
    q_encoded = pool(cnn(q))
    q_v_att = pool(tf.keras.layers.Attention()([cnn(q), cnn(p)]))
    return tf.keras.layers.Concatenate()([q_encoded, q_v_att])

cnn1 = cnn('cnn_layer_1')
stacked_inputs = stack_inputs(inp1, inp2)
map_result = tf.keras.backend.map_fn(lambda x: attention_op(x[0], x[1], cnn1), stacked_inputs, dtype=tf.float32)

Upvotes: 0

Manuel Cuevas
Manuel Cuevas

Reputation: 339

If Tensors are of the same shape (most cases), stack the tensors in the first dimension and slide them inside the map function: 

import tensorflow as tf
# declare variables
a = tf.constant([1, 2, 3, 4])
b = tf.constant([17, 12, 11, 10])

# NOTE: use stack because map_tf only takes one input tensor
ab = tf.stack([a, b], 1)


def map_operation(value_ab):
    # iterates each value_ab
    value_a = value_ab[0]
    value_b = value_ab[1]
    return value_a+value_b

# print(map(lambda x,y:x+y, a,b)) # ==> [18, 14, 14, 14]
# iterates each value_ab at map_operation()
map_result = tf.map_fn(map_operation, ab, dtype=tf.int32)

with tf.Session() as sess:
    tf.initialize_all_variables().run()
    print(sess.run(map_result))   # [18 14 14 14]

reference LINK

Upvotes: 5

A Das
A Das

Reputation: 847

As on today, I see that map_fn is enhanced to take two tensors as the documentation says that - "elems: A tensor or (possibly nested) sequence of tensors, each of which will be unpacked along their first dimension. The nested sequence of the resulting slices will be applied to fn." The example (though given in numpy form) also shows that it can take two tensors. I'm copying it here.

elems = (np.array([1, 2, 3]), np.array([-1, 1, -1]))
alternate = map_fn(lambda x: x[0] * x[1], elems, dtype=tf.int64)
# alternate == [-1, 2, -3]

Source:

Upvotes: 18

jbbj94
jbbj94

Reputation: 334

Not natively, but here's a quick function that achieves it:

def map(fn, arrays, dtype=tf.float32):
    # assumes all arrays have same leading dim
    indices = tf.range(tf.shape(arrays[0])[0])
    out = tf.map_fn(lambda ii: fn(*[array[ii] for array in arrays]), indices, dtype=dtype)
    return out

# example: batch affine tranformation
x = tf.random_normal([4,5,6])
M = tf.random_normal([4,6,10])
b = tf.random_normal([4,10])

f = lambda x0,M0,b0: tf.matmul(x0,M0) + b0
batch_y = map(f, [x,M,b])

Upvotes: 5

Sung Kim
Sung Kim

Reputation: 8536

The source code shows that this function takes only one elems tensor:

def map_fn(fn, elems, dtype=None, parallel_iterations=10, back_prop=True,
       swap_memory=False, name=None):

I don't see any * and ** parameters.

Upvotes: 1

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