示例#1
0
文件: function.py 项目: gburt/iaf
 def func(data, n_batch=0, randomorder=True, data_global={}):
     data = ndict.ordered(ndict.flatten(data))
     data_global = ndict.ordered(ndict.flatten(data_global))        
     # Check if keys of 'x' and 'inputs' match
     allkeys = (data.keys() + data_global.keys())
     for i in range(len(data)):
         if x.keys()[i] not in allkeys:
             raise Exception('Non-matching keys:'+str(allkeys)+' vs. '+str(x.keys()))
     # Compile function if not already done
     if f[0] == None:
         _compile()
     if n_batch <= 0:
         # Get results
         _data = data.copy()
         _data.update(data_global)
         inputs_ordered = ndict.orderedvals((_data,))
         _result = f[0](*inputs_ordered)
         # Put it in a dictionary with the corresponding keys
         result = {y.keys()[i]: _result[i] for i in range(len(y))}
     else:
         # Minibatch-based evaluation.
         # This assumes that input and output are tensors, and the first dimension iterates of datapoints
         n_tot = data.itervalues().next().shape[0]
         n_minibatches = int(math.ceil(1. * n_tot / n_batch))
         
         n_tile = 1
         if n_batch > n_tot:
             assert n_batch%n_tot == 0
             n_tile = n_batch/n_tot
             
         indices = np.tile(np.arange(n_tot),n_tile)
         if randomorder:
             np.random.shuffle(indices)
             adict = dict(zip(np.tile(np.arange(n_tot),n_tile),indices))
             indices_inverse = sorted(adict, key=adict.get)
         
         results = []
         for i in range(n_minibatches):
             data_minibatch = ndict.getRowsFromIndices(data, indices[i*n_batch:(i+1)*n_batch])
             data_minibatch.update(data_global)
             inputs_ordered = ndict.orderedvals((data_minibatch,))
             results.append(f[0](*inputs_ordered))
             if _debug:
                 print 'Function debug', i, results[-1]
             if checknan == 'raise':
                 if np.isnan(np.sum(results[-1])):
                     print results[-1]
                     raise Exception("NaN detected")
         result = {y.keys()[i]: np.concatenate([results[j][i] for j in range(n_minibatches)]) for i in range(len(y))}
         if randomorder:
             result = ndict.getRowsFromIndices(result, indices_inverse)
     
     result = OrderedDict(sorted(result.items()))
     
     # Return result
     #raise Exception()
     if return_single_y:
         return result[result.keys()[0]]
     return result
示例#2
0
    def func(data, n_batch=0, randomorder=True, data_global={}):
        data = ndict.ordered(ndict.flatten(data))
        data_global = ndict.ordered(ndict.flatten(data_global))
        # Check if keys of 'x' and 'inputs' match
        allkeys = (data.keys() + data_global.keys())
        for i in range(len(data)):
            if x.keys()[i] not in allkeys:
                raise Exception('Non-matching keys:' + str(allkeys) + ' vs. ' +
                                str(x.keys()))
        # Compile function if not already done
        if f[0] == None:
            _compile()
        if n_batch <= 0:
            # Get results
            _data = data.copy()
            _data.update(data_global)
            inputs_ordered = ndict.orderedvals((_data, ))
            _result = f[0](*inputs_ordered)
            # Put it in a dictionary with the corresponding keys
            result = {y.keys()[i]: _result[i] for i in range(len(y))}
        else:
            # Minibatch-based evaluation.
            # This assumes that input and output are tensors, and the first dimension iterates of datapoints
            n_tot = data.itervalues().next().shape[0]
            n_minibatches = int(math.ceil(1. * n_tot / n_batch))

            n_tile = 1
            if n_batch > n_tot:
                assert n_batch % n_tot == 0
                n_tile = n_batch / n_tot

            indices = np.tile(np.arange(n_tot), n_tile)
            if randomorder:
                np.random.shuffle(indices)
                adict = dict(zip(np.tile(np.arange(n_tot), n_tile), indices))
                indices_inverse = sorted(adict, key=adict.get)

            results = []
            for i in range(n_minibatches):
                data_minibatch = ndict.getRowsFromIndices(
                    data, indices[i * n_batch:(i + 1) * n_batch])
                data_minibatch.update(data_global)
                inputs_ordered = ndict.orderedvals((data_minibatch, ))
                results.append(f[0](*inputs_ordered))
                if _debug:
                    print 'Function debug', i, results[-1]
                if checknan == 'raise':
                    if np.isnan(np.sum(results[-1])):
                        print results[-1]
                        raise Exception("NaN detected")
            result = {
                y.keys()[i]:
                np.concatenate([results[j][i] for j in range(n_minibatches)])
                for i in range(len(y))
            }
            if randomorder:
                result = ndict.getRowsFromIndices(result, indices_inverse)

        result = OrderedDict(sorted(result.items()))

        # Return result
        #raise Exception()
        if return_single_y:
            return result[result.keys()[0]]
        return result
示例#3
0
def function(x, y, lazy=False, _debug=False, checknan='raise', **kwargs):
    # Default keyword arguments
    if not kwargs.has_key('on_unused_input'):
        kwargs['on_unused_input'] = 'warn'
    if not kwargs.has_key('mode'):
        kwargs['mode'] = default_function_mode
    # Order the input dict
    x = ndict.ordered(ndict.flatten(x))
    # Check the output dict
    return_single_y = False
    if not isinstance(y, dict):
        return_single_y = True
        y = {str(y): y}
    y = ndict.ordered(y)
    # Lazily compiled function (saves a lot of time)
    f = [None]

    def _compile(verbose=True):
        t0 = time.time()
        print 'Compiling... ',
        #print '[graphy] Compiling function '+str(x.keys())+' => '+str(y.keys())+' ...'
        sys.stdout.flush()
        f[0] = theano.function(x.values(), y.values(), **kwargs)
        print "%.2f" % (time.time() - t0), 's'

    if not lazy:
        _compile()
    # The function to be called
    def func(data, n_batch=0, randomorder=True, data_global={}):
        data = ndict.ordered(ndict.flatten(data))
        data_global = ndict.ordered(ndict.flatten(data_global))
        # Check if keys of 'x' and 'inputs' match
        allkeys = (data.keys() + data_global.keys())
        for i in range(len(data)):
            if x.keys()[i] not in allkeys:
                raise Exception('Non-matching keys:' + str(allkeys) + ' vs. ' +
                                str(x.keys()))
        # Compile function if not already done
        if f[0] == None:
            _compile()
        if n_batch <= 0:
            # Get results
            _data = data.copy()
            _data.update(data_global)
            inputs_ordered = ndict.orderedvals((_data, ))
            _result = f[0](*inputs_ordered)
            # Put it in a dictionary with the corresponding keys
            result = {y.keys()[i]: _result[i] for i in range(len(y))}
        else:
            # Minibatch-based evaluation.
            # This assumes that input and output are tensors, and the first dimension iterates of datapoints
            n_tot = data.itervalues().next().shape[0]
            n_minibatches = int(math.ceil(1. * n_tot / n_batch))

            n_tile = 1
            if n_batch > n_tot:
                assert n_batch % n_tot == 0
                n_tile = n_batch / n_tot

            indices = np.tile(np.arange(n_tot), n_tile)
            if randomorder:
                np.random.shuffle(indices)
                adict = dict(zip(np.tile(np.arange(n_tot), n_tile), indices))
                indices_inverse = sorted(adict, key=adict.get)

            results = []
            for i in range(n_minibatches):
                data_minibatch = ndict.getRowsFromIndices(
                    data, indices[i * n_batch:(i + 1) * n_batch])
                data_minibatch.update(data_global)
                inputs_ordered = ndict.orderedvals((data_minibatch, ))
                results.append(f[0](*inputs_ordered))
                if _debug:
                    print 'Function debug', i, results[-1]
                if checknan == 'raise':
                    if np.isnan(np.sum(results[-1])):
                        print results[-1]
                        raise Exception("NaN detected")
            result = {
                y.keys()[i]:
                np.concatenate([results[j][i] for j in range(n_minibatches)])
                for i in range(len(y))
            }
            if randomorder:
                result = ndict.getRowsFromIndices(result, indices_inverse)

        result = OrderedDict(sorted(result.items()))

        # Return result
        #raise Exception()
        if return_single_y:
            return result[result.keys()[0]]
        return result

    # Return the func
    return G.Struct(__call__=func, f=f)
示例#4
0
文件: function.py 项目: gburt/iaf
def function(x, y, lazy=False, _debug=False, checknan='raise', **kwargs):
    # Default keyword arguments
    if not kwargs.has_key('on_unused_input'):
        kwargs['on_unused_input'] = 'warn'
    if not kwargs.has_key('mode'):
        kwargs['mode'] = default_function_mode
    # Order the input dict
    x = ndict.ordered(ndict.flatten(x))
    # Check the output dict
    return_single_y = False
    if not isinstance(y, dict):
        return_single_y = True
        y = {str(y): y}
    y = ndict.ordered(y)
    # Lazily compiled function (saves a lot of time)
    f = [None]
    def _compile(verbose=True):
        t0 = time.time()
        print 'Compiling... ',
        #print '[graphy] Compiling function '+str(x.keys())+' => '+str(y.keys())+' ...'
        sys.stdout.flush()
        f[0] = theano.function(x.values(), y.values(), **kwargs)
        print "%.2f" % (time.time()-t0), 's'
    if not lazy:
        _compile()
    # The function to be called
    def func(data, n_batch=0, randomorder=True, data_global={}):
        data = ndict.ordered(ndict.flatten(data))
        data_global = ndict.ordered(ndict.flatten(data_global))        
        # Check if keys of 'x' and 'inputs' match
        allkeys = (data.keys() + data_global.keys())
        for i in range(len(data)):
            if x.keys()[i] not in allkeys:
                raise Exception('Non-matching keys:'+str(allkeys)+' vs. '+str(x.keys()))
        # Compile function if not already done
        if f[0] == None:
            _compile()
        if n_batch <= 0:
            # Get results
            _data = data.copy()
            _data.update(data_global)
            inputs_ordered = ndict.orderedvals((_data,))
            _result = f[0](*inputs_ordered)
            # Put it in a dictionary with the corresponding keys
            result = {y.keys()[i]: _result[i] for i in range(len(y))}
        else:
            # Minibatch-based evaluation.
            # This assumes that input and output are tensors, and the first dimension iterates of datapoints
            n_tot = data.itervalues().next().shape[0]
            n_minibatches = int(math.ceil(1. * n_tot / n_batch))
            
            n_tile = 1
            if n_batch > n_tot:
                assert n_batch%n_tot == 0
                n_tile = n_batch/n_tot
                
            indices = np.tile(np.arange(n_tot),n_tile)
            if randomorder:
                np.random.shuffle(indices)
                adict = dict(zip(np.tile(np.arange(n_tot),n_tile),indices))
                indices_inverse = sorted(adict, key=adict.get)
            
            results = []
            for i in range(n_minibatches):
                data_minibatch = ndict.getRowsFromIndices(data, indices[i*n_batch:(i+1)*n_batch])
                data_minibatch.update(data_global)
                inputs_ordered = ndict.orderedvals((data_minibatch,))
                results.append(f[0](*inputs_ordered))
                if _debug:
                    print 'Function debug', i, results[-1]
                if checknan == 'raise':
                    if np.isnan(np.sum(results[-1])):
                        print results[-1]
                        raise Exception("NaN detected")
            result = {y.keys()[i]: np.concatenate([results[j][i] for j in range(n_minibatches)]) for i in range(len(y))}
            if randomorder:
                result = ndict.getRowsFromIndices(result, indices_inverse)
        
        result = OrderedDict(sorted(result.items()))
        
        # Return result
        #raise Exception()
        if return_single_y:
            return result[result.keys()[0]]
        return result
    # Return the func
    return G.Struct(__call__=func, f=f)