import tensorflow as tf
tf.enable_eager_execution() # to check the content
tf.convert_to_tensor([[1,2]])
# no equivalent to empty but placeholders:
tf.placeholder(tf.float32, shape=(5,3))
tf.ones((5,3))
tf.zeros((5,3))
tf.random_uniform((5,3))
tf.random_normal((5,3))
tf.get_shape(tf.random_normal((5,3))) # static
tf.shape(tf.random_normal((5,3))) # dynamic
tf.matmul(tf.random_normal((5,3)), tf.random_uniform((3,4)))
tf.reshape(tf.random_normal((5,3)), (3,5))
tf.transpose(tf.random_normal((5,3)), (1,0))
tf.concat([tf.random_normal((5,3)), tf.random_uniform((5,4))], 1)
tf.stack([tf.random_normal((5,4)), tf.random_uniform((5,4))], 1)
tf.expand_dims(tf.ones((5,3)),1)
tf.squeeze(tf.ones((5,1,3)),1)
tf.range(0,10)
tf.reduce_max(tf.random_uniform((5,3)))
tf.reduce_max(tf.random_uniform((5,3)),axis=1)
tf.math.maximum(tf.random_uniform((5,3)), tf.random_normal((5,3)))