Newb here just getting into Python and ran into an issue that's beating me down. I have the following excerpt of Python code to create a PPT slide from an existing template. The layout and placeholders are correct but I can't get it to run with my data listed below (x, y_in, & y_out). Any help is greatly appreciated.
x = [datetime.datetime(2017, 8, 4, 15, 5, tzinfo=<FixedOffset u'+00:00' datetime.timedelta(0)>), datetime.datetime(2017, 8, 4, 15, 10, tzinfo=<FixedOffset u'+00:00' datetime.timedelta(0)>), datetime.datetime(2017, 8, 4, 15, 15, tzinfo=<FixedOffset u'+00:00' datetime.timedelta(0)>), datetime.datetime(2017, 8, 4, 15, 20, tzinfo=<FixedOffset u'+00:00' datetime.timedelta(0)>)]
y_in = [780993, 538962, 730180, 1135936]
y_out = [5631489, 6774738, 6485944, 6611580]
prs = Presentation('Network_Utilization_template_master.pptx')
slide = prs.slides.add_slide(prs.slide_layouts[2])
placeholder = slide.placeholders[17]
chart_data = CategoryChartData()
chart_data.categories = x
chart_data.add_series(y_in)
chart_data.add_series(y_out)
graphic_frame = placeholder.insert_chart(XL_CHART_TYPE.LINE, chart_data)
chart = graphic_frame.chart
chart.has_legend = True
chart.legend.include_in_layout = True
chart.series[0-2].smooth = True
prs.save("Network_Utilization_" + today_s + ".pptx")
the compiler spits out the following:
Traceback (most recent call last):
File "/Users/jemorey/Documents/pptx-2.py", line 81, in <module>
graphic_frame = placeholder.insert_chart(XL_CHART_TYPE.LINE, chart_data)
File "/Users/jemorey/Library/Python/2.7/lib/python/site-packages/pptx/shapes/placeholder.py", line 291, in insert_chart
rId = self.part.add_chart_part(chart_type, chart_data)
File "/Users/jemorey/Library/Python/2.7/lib/python/site-packages/pptx/parts/slide.py", line 174, in add_chart_part
chart_part = ChartPart.new(chart_type, chart_data, self.package)
File "/Users/jemorey/Library/Python/2.7/lib/python/site-packages/pptx/parts/chart.py", line 29, in new
chart_blob = chart_data.xml_bytes(chart_type)
File "/Users/jemorey/Library/Python/2.7/lib/python/site-packages/pptx/chart/data.py", line 104, in xml_bytes
return self._xml(chart_type).encode('utf-8')
File "/Users/jemorey/Library/Python/2.7/lib/python/site-packages/pptx/chart/data.py", line 128, in _xml
return ChartXmlWriter(chart_type, self).xml
File "/Users/jemorey/Library/Python/2.7/lib/python/site-packages/pptx/chart/xmlwriter.py", line 803, in xml
'ser_xml': self._ser_xml,
File "/Users/jemorey/Library/Python/2.7/lib/python/site-packages/pptx/chart/xmlwriter.py", line 902, in _ser_xml
'tx_xml': xml_writer.tx_xml,
File "/Users/jemorey/Library/Python/2.7/lib/python/site-packages/pptx/chart/xmlwriter.py", line 191, in tx_xml
'series_name': self.name,
File "/Users/jemorey/Library/Python/2.7/lib/python/site-packages/pptx/chart/xmlwriter.py", line 121, in name
return escape(self._series.name)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/xml/sax/saxutils.py", line 32, in escape
data = data.replace("&", "&")
AttributeError: 'list' object has no attribute 'replace'
David Zemens is quite right in his comment. A series has a name, which appears as the first argument to ChartData.add_series(). The name appears in the legend next to the line color for that series and also appears as the column heading for the data for that series. Adding that in should get you to your next step.
Something like:
chart_data.add_series('MB in', y_in)
chart_data.add_series('MB out', y_out)
Related
I'm trying to create my model with several conv3d lstm cell layers:
I run the following code:
conv1, state1 = conv3d('conv1', _X, [8,112,112,1], [3,3,3], 64)
pool1 = max_pool('pool1', conv1, k=1)
conv2, state2 = conv3d('conv2', pool1, [8, 56, 56, 64], [3, 3, 3], 128)
pool2 = max_pool('pool2', conv2, k=2)
The conv3d functions:
def conv3d(myname, l_input, shape, kernel, outchan):
cell = contrib_rnn_cell.Conv3DLSTMCell(input_shape=shape, output_channels=out$
hidden = cell.zero_state(array_ops.shape(l_input)[0], dtypes.float32)
output, state = cell(l_input, hidden)
print(output.shape)
return output, state
My code runs OK for the conv1 and pool1 but for conv2 layer it shows me an error:
Traceback (most recent call last):
File "conv3dlstm.py", line 272, in <module>
run(16)
File "conv3dlstm.py", line 199, in run
biases)
File "/home/user/projects/model_conv3dlstm.py", line 47, in inference_c3d
conv2, state2 = conv3d('conv2', pool1, [8, 56, 56, 64], [3, 3, 3], 128)
File "/home/user/projects/model_conv3dlstm.py", line 32, in conv3d
output, state = cell(l_input, hidden)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 190, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.py", line 696, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/rnn/python/ops/rnn_cell.py", line 2110, in call
4 * self._output_channels, self._use_bias)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/rnn/python/ops/rnn_cell.py", line 2200, in _conv
"kernel", filter_size + [total_arg_size_depth, num_features], dtype=dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 1297, in get_variable
constraint=constraint)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 1093, in get_variable
constraint=constraint)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 431, in get_variable
return custom_getter(**custom_getter_kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 193, in _rnn_get_variable
variable = getter(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 408, in _true_getter
use_resource=use_resource, constraint=constraint)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 747, in _get_single_variable
name, "".join(traceback.format_list(tb))))
ValueError: Variable conv_lstm_cell/kernel already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/rnn/python/ops/rnn_cell.py", line 2200, in _conv
"kernel", filter_size + [total_arg_size_depth, num_features], dtype=dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/rnn/python/ops/rnn_cell.py", line 2110, in call
4 * self._output_channels, self._use_bias)
File "/home/user/projects/model_conv3dlstm.py", line 32, in conv3d
output, state = cell(l_input, hidden)
I saw the code in run_cell.py at line 2200 which is:
kernel = vs.get_variable(
"kernel", filter_size + [total_arg_size_depth, num_features], dtype=dtype)
Which is getting variable with fixed name "kernel". If I understand it correctly, it is supposed to be unique. But it means I can't create more of Conv3DLSTMCells than one? Is it a bug or am I using it incorrectly?
I have an issue that seems to have no straight forward solution in Keras.
My server runs on ubuntu 14.04, keras with backend tensorflow.
Here's the issue:
I have two input tenors of the shape: Input(shape=(30,125,1)), each of them is fed to a cascade of three layers below:
CNN1 = Conv2D(filters = 8, kernel_size = (1,64) , padding = "same" , activation = "relu" )
CNN2 = Conv2D(filters = 8, kernel_size = (8,1) , padding = "same" , activation = "relu" )
pool = MaxPooling2D((2, 2))
Each of the obtained output tensors for respective inputs is of shape (None, 15, 62, 8). Now, I wish to add each of the (15,62) matrix for both inputs for each filter and get an output of dimension again (None, 15, 62, 8).
I tried with the following lines of code using Lambda layer but it throws an error.
from keras import backend as K
from keras.layers import Lambda
def myadd(x):
increment = x[1]
result = K.update_add(x[0], increment)
return result
in_1 = Input(shape=(30,125,1))
in_1CNN1 = CNN1(in_1)
in_1CNN2 = CNN2(in_1CNN1)
in_1pool = pool(in_1CNN2)
in_2 = Input(shape=(30,125,1))
in_2CNN1 = CNN1(in_2)
in_2CNN2 = CNN2(in_2CNN1)
in_2pool = pool(in_2CNN2)
y1 =y1.astype(np.float32) # an input regression label array of shape (numsamples,1) loaded from a mat file
out1 = Lambda(myadd, output_shape=(None, 15, 62, 8))([in_1pool,in_2pool])
a= keras.layers.Flatten()(out1)
pre1 = Dense(1000, activation='sigmoid')(a)
pre2 =Dropout(0.2)(pre1)
predictions = Dense(1, activation='sigmoid')(pre2)
model = Model(inputs=[in_1,in_2], outputs=predictions)
model.compile(optimizer='sgd',loss='mean_squared_error')
model.fit([inputdata1,inputdata2], y1, epochs=20, validation_split=0.5)
#inputdata1, inputdata2 are arrays loaded from a mat file and are each of shape (5169, 30, 125, 1)
The error is highlighted below:
Traceback (most recent call last):
File "keras_workshop/keras_multipleinputs_multiple CNN.py", line 225, in <module>
out1 = Lambda(myadd, output_shape=(None, 15, 62, 8))([in_1pool,in_2pool])
File "/home/tharun/anaconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 603, in __call__
output = self.call(inputs, **kwargs)
File "/home/tharun/anaconda2/lib/python2.7/site-packages/keras/layers/core.py", line 651, in call
return self.function(inputs, **arguments)
File "keras_workshop/keras_multipleinputs_multiple CNN.py", line 75, in myadd
result = K.update_add(x[0], increment)
File "/home/tharun/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 958, in update_add
return tf.assign_add(x, increment)
File "/home/tharun/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/state_ops.py", line 245, in assign_add
return ref.assign_add(value)
AttributeError: 'Tensor' object has no attribute 'assign_add'
Try the Add() layer or the add() function that Keras provides.
Add
keras.layers.Add()
Layer that adds a list of inputs.
It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
add
keras.layers.add(inputs)
Functional interface to the Add layer.
Arguments
inputs: A list of input tensors (at least 2).
**kwargs: Standard layer keyword arguments.
Returns
A tensor, the sum of the inputs.
Specificly, I use Python2.7. I read and print the two data frames from Quandl: 'FMAC/HPI_AK' and 'FMAC/HPI_CA' individually with no problem. I used merged = pd.merge(df1, df2, on = 'Date', how = 'outer') to merge the two data frames. But when I tried to merge the two data frames, I get a traceback saying keyerror: 'Date' where 'Date' is the attribute in the first/index column in both data frames.
import quandl
import pandas as pd
api_key = open('quandlapikey.txt', 'r').read()
df1 = quandl.get('FMAC/HPI_ak', authtoken=api_key)
df2 = quandl.get('FMAC/HPI_ca', authtoken=api_key)
print(df1.head())
print(df2.head())
merged = pd.merge(df1, df2, on = 'Date', how = 'outer')
merged.set_index('Date', inplace = True)
print(merged)
Date Value
1975-01-31 15.671711
1975-02-28 15.726897
1975-03-31 15.919058
1975-04-30 16.233030
1975-05-31 16.494823
Date Value
1975-01-31 34.447924
1975-02-28 34.958144
1975-03-31 35.480144
1975-04-30 36.024334
1975-05-31 36.617578
Traceback (most recent call last):
File "", line 1, in
runfile('/Users/hans/Desktop/sentdex/buildingdataset.py', wdir='/Users/hans/Desktop/sentdex')
File "/Users/hans/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 866, in runfile
execfile(filename, namespace)
File "/Users/hans/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 94, in execfile
builtins.execfile(filename, *where)
File "/Users/hans/Desktop/sentdex/buildingdataset.py", line 22, in
merged = pd.merge(df1, df2, on = 'Date', how = 'outer')
File "/Users/hans/anaconda2/lib/python2.7/site-packages/pandas/tools/merge.py", line 61, in merge
copy=copy, indicator=indicator)
File "/Users/hans/anaconda2/lib/python2.7/site-packages/pandas/tools/merge.py", line 543, in init
self.join_names) = self._get_merge_keys()
File "/Users/hans/anaconda2/lib/python2.7/site-packages/pandas/tools/merge.py", line 810, in _get_merge_keys
right_keys.append(right[rk]._values)
File "/Users/hans/anaconda2/lib/python2.7/site-packages/pandas/core/frame.py", line 2059, in getitem
return self._getitem_column(key)
File "/Users/hans/anaconda2/lib/python2.7/site-packages/pandas/core/frame.py", line 2066, in _getitem_column
return self._get_item_cache(key)
File "/Users/hans/anaconda2/lib/python2.7/site-packages/pandas/core/generic.py", line 1386, in _get_item_cache
values = self._data.get(item)
File "/Users/hans/anaconda2/lib/python2.7/site-packages/pandas/core/internals.py", line 3543, in get
loc = self.items.get_loc(item)
File "/Users/hans/anaconda2/lib/python2.7/site-packages/pandas/indexes/base.py", line 2136, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/index.pyx", line 132, in pandas.index.IndexEngine.get_loc (pandas/index.c:4433)
File "pandas/index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas/index.c:4279)
File "pandas/src/hashtable_class_helper.pxi", line 732, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13742)
File "pandas/src/hashtable_class_helper.pxi", line 740, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13696)
KeyError: 'Date'
You're getting that error because Date is an index in those DataFrames not a column.
You can instead do (tested):
merged = pd.merge(df1, df2, how='outer', left_index=True, right_index=True)
I saved model and weights in Keras and then try to load them ,but it shows that Invalid initialization: my_init.How can I fix the problem?
model = Sequential()
def my_init(shape, name=None):
return initializations.normal(shape, scale=0.1, name=name)
def m6_1():
model.add(Convolution2D(32, 3, 3, init=my_init))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3, init=my_init))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(256, init=my_init))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
save model and weights
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
model.save_weights("model.h5")
load model and weights
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json,custom_objects={'my_init':my_init})
loaded_model.load_weights("model.h5")
error messageTraceback (most recent call last):
File "revised_learn_ETL6_load_model.py", line 73, in <module>
loaded_model = model_from_json(loaded_model_json,custom_objects={"my_init": my_init})
File "/home/ubuntu/.env/local/lib/python2.7/site-packages/keras/models.py", line 197, in model_from_json
return layer_from_config(config, custom_objects=custom_objects)
File "/home/ubuntu/.env/local/lib/python2.7/site-packages/keras/utils/layer_utils.py", line 36, in layer_from_config
return layer_class.from_config(config['config'])
File "/home/ubuntu/.env/local/lib/python2.7/site-packages/keras/models.py", line 1019, in from_config
layer = get_or_create_layer(first_layer)
File "/home/ubuntu/.env/local/lib/python2.7/site-packages/keras/models.py", line 1003, in get_or_create_layer
layer = layer_from_config(layer_data)
File "/home/ubuntu/.env/local/lib/python2.7/site-packages/keras/utils/layer_utils.py", line 36, in layer_from_config
return layer_class.from_config(config['config'])
File "/home/ubuntu/.env/local/lib/python2.7/site-packages/keras/engine/topology.py", line 929, in from_config
return cls(**config)
File "/home/ubuntu/.env/local/lib/python2.7/site-packages/keras/layers/convolutional.py", line 381, in __init__
self.init = initializations.get(init, dim_ordering=dim_ordering)
File "/home/ubuntu/.env/local/lib/python2.7/site-packages/keras/initializations.py", line 107, in get
'initialization', kwargs=kwargs)
File "/home/ubuntu/.env/local/lib/python2.7/site-packages/keras/utils/generic_utils.py", line 16, in get_from_module
str(identifier))
Exception: Invalid initialization: my_init
I am facing a problem when I start my trainer and I can't figure out the cause.
My input data is of dimension 42 and my output should be one value out of 4.
This is the shape of my training and test set:
Training set:
input = (1152, 42) target = (1152,)
Training set: input = (1152, 42) target = (1152,)
Test set: input = (384, 42) target = (384,)
This is the construction of my network:
ls = MS.GradientDescent(lr=0.01)
cost = MC.CrossEntropy()
i = ML.Input(42, name='inp')
h = ML.Hidden(23, activation=MA.Sigmoid(), initializations=[MI.GlorotTanhInit()], name="hid")
o = ML.SoftmaxClassifier(4, learningScenario=ls, costObject=cost, name="out")
mlp = i > h > o
And this is the construction of the datasets, trainers and recorders:
trainData = MDM.RandomSeries(distances = train_set[0], next_state = train_set[1])
trainMaps = MDM.DatasetMapper()
trainMaps.mapInput(i, trainData.distances)
trainMaps.mapOutput(o, trainData.next_state)
testData = MDM.RandomSeries(distances = test_set[0], next_state = test_set[1])
testMaps = MDM.DatasetMapper()
testMaps.mapInput(i, testData.distances)
testMaps.mapOutput(o, testData.next_state)
earlyStop = MSTOP.GeometricEarlyStopping(testMaps, patience=100, patienceIncreaseFactor=1.1, significantImprovement=0.00001, outputFunction="score", outputLayer=o)
epochWall = MSTOP.EpochWall(1000)
trainer = MT.DefaultTrainer(
trainMaps=trainMaps,
testMaps=testMaps,
validationMaps=None,
stopCriteria=[earlyStop, epochWall],
testFunctionName="testAndAccuracy",
trainMiniBatchSize=MT.DefaultTrainer.ALL_SET,
saveIfMurdered=False
)
recorder = MREC.GGPlot2("MLP", whenToSave = [MREC.SaveMin("test", o.name, "score")], printRate=1, writeRate=1)
trainer.start("MLP", mlp, recorder = recorder)
But the following error is being produced:
Traceback (most recent call last):
File "nn-mariana.py", line 82, in <module>
trainer.start("MLP", mlp, recorder = recorder)
File "SUPRESSED/Mariana/Mariana/training/trainers.py", line 226, in start
Trainer_ABC.start( self, runName, model, recorder, trainingOrder, moreHyperParameters )
File "SUPRESSED/Mariana/Mariana/training/trainers.py", line 110, in start
return self.run(runName, model, recorder, *args, **kwargs)
File "SUPRESSED/Mariana/Mariana/training/trainers.py", line 410, in run
outputLayers
File "SUPRESSED/Mariana/Mariana/training/trainers.py", line 269, in _trainTest
res = modelFct(output, **kwargs)
File "SUPRESSED/Mariana/Mariana/network.py", line 47, in __call__
return self.callTheanoFct(outputLayer, **kwargs)
File "SUPRESSED/Mariana/Mariana/network.py", line 44, in callTheanoFct
return self.outputFcts[ol](**kwargs)
File "SUPRESSED/Mariana/Mariana/wrappers.py", line 110, in __call__
return self.run(**kwargs)
File "SUPRESSED/Mariana/Mariana/wrappers.py", line 102, in run
fres = iter(self.theano_fct(*self.fctInputs.values()))
File "SUPRESSED/Theano/theano/compile/function_module.py", line 871, in __call__
storage_map=getattr(self.fn, 'storage_map', None))
File "SUPRESSED/Theano/theano/gof/link.py", line 314, in raise_with_op
reraise(exc_type, exc_value, exc_trace)
File "SUPRESSED/Theano/theano/compile/function_module.py", line 859, in __call__
outputs = self.fn()
ValueError: Input dimension mis-match. (input[0].shape[1] = 1152, input[1].shape[1] = 4)
Apply node that caused the error: Elemwise{Composite{((i0 * i1) + (i2 * log(i3)))}}[(0, 1)](InplaceDimShuffle{x,0}.0, LogSoftmax.0, Elemwise{sub,no_inplace}.0, Elemwise{sub,no_inplace}.0)
Toposort index: 18
Inputs types: [TensorType(int32, row), TensorType(float64, matrix), TensorType(int32, row), TensorType(float64, matrix)]
Inputs shapes: [(1, 1152), (1152, 4), (1, 1152), (1152, 4)]
Inputs strides: [(4608, 4), (32, 8), (4608, 4), (32, 8)]
Inputs values: ['not shown', 'not shown', 'not shown', 'not shown']
Outputs clients: [[Sum{axis=[1], acc_dtype=float64}(Elemwise{Composite{((i0 * i1) + (i2 * log(i3)))}}[(0, 1)].0)]]
Versions:
Mariana (1.0.1rc1, /media/guilhermevrs/Data/Documentos/Academico/TCC-code/Mariana)
Theano (0.8.0.dev0, SUPRESSED/Theano)
This code was produced having as base the tutorial code from the mnist example.
Could you please help me to figure out what's going on?
Thank you in advance
I talked directly to the authors of Mariana and the cause and solution is explained in this issue