I'm trying to develop a Bayesian model using Pystan. I'm able to compile the model successfully. But when I'm sampling data I'm getting run time error. Refer to the code below:
my_code = '''
data {
int N;
int K1;
int K2;
real max_intercept;
matrix[N, K1] X1;
matrix[N, K2] X2;
vector[N] y;
}
parameters {
vector<lower=0>[K1] beta1;
vector[K2] beta2;
real<lower=0, upper=max_intercept> alpha;
real<lower=0> noise_var;
}
model {
beta1 ~ normal(0, 1);
beta2 ~ normal(0, 1);
noise_var ~ inv_gamma(0.05, 0.05 * 0.01);
y ~ normal(X1*beta1 + X2*beta2 + alpha, sqrt(noise_var));
}
'''
fit1 = sm1.sampling(data=input_data, iter=2000, chains=4, init=0.5,n_jobs=-1) #Getting an error here
I have checked all the data points (no missing data or no column with same number through out) and their data types (all are float 64). I also scaled the data using MinMaxScaler
input_data = {
'N': len(data_scaled), #836
'K1': len(pos_var), #17
'K2': len(pos_neg_var),#29
'X1': X1, #(836,17)
'X2': X2, #(836,17)
'y': data['orders'].values,
'max_intercept': min(data['orders']) #0
}
Below is the error I'm getting.
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "C:\Users\abc\.conda\envs\stan_env\lib\multiprocessing\pool.py", line 121, in worker
result = (True, func(*args, **kwds))
File "C:\Users\abc\.conda\envs\stan_env\lib\multiprocessing\pool.py", line 44, in mapstar
return list(map(*args))
File "stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893.pyx", line 371, in stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893._call_sampler_star
File "stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893.pyx", line 404, in stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893._call_sampler
RuntimeError: Initialization failed.
"""
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
<timed exec> in <module>
~\.conda\envs\stan_env\lib\site-packages\pystan\model.py in sampling(self, data, pars, chains, iter, warmup, thin, seed, init, sample_file, diagnostic_file, verbose, algorithm, control, n_jobs, **kwargs)
776 call_sampler_args = izip(itertools.repeat(data), args_list, itertools.repeat(pars))
777 call_sampler_star = self.module._call_sampler_star
--> 778 ret_and_samples = _map_parallel(call_sampler_star, call_sampler_args, n_jobs)
779 samples = [smpl for _, smpl in ret_and_samples]
780
~\.conda\envs\stan_env\lib\site-packages\pystan\model.py in _map_parallel(function, args, n_jobs)
83 try:
84 pool = multiprocessing.Pool(processes=n_jobs)
---> 85 map_result = pool.map(function, args)
86 finally:
87 pool.close()
~\.conda\envs\stan_env\lib\multiprocessing\pool.py in map(self, func, iterable, chunksize)
266 in a list that is returned.
267 '''
--> 268 return self._map_async(func, iterable, mapstar, chunksize).get()
269
270 def starmap(self, func, iterable, chunksize=None):
~\.conda\envs\stan_env\lib\multiprocessing\pool.py in get(self, timeout)
655 return self._value
656 else:
--> 657 raise self._value
658
659 def _set(self, i, obj):
RuntimeError: Initialization failed.
I'm relatively new to Pystan. I appreciate any guidance I get here.
I fixed the issue! Runtime error generally comes when the data is not meeting the constraints defined in the model.
For instance X values having some -ve numbers when the constraint is X>0 defined in the model.
Also most common mistake, need to make sure Y values are not off. In my data there are few Y values that 0, these values passed missing values and pos value checks. Upon imputing the values with mean of Y the problem is resolved.
Happy learning!
Related
I am getting the following error in my Stan code:
SYNTAX ERROR, MESSAGE(S) FROM PARSER:
No matches for:
gpareto_lcdf(real, real, real)
Available argument signatures for gpareto_lcdf:
gpareto_lcdf(vector, real, real)
error in 'modelafda6ff99d79_gpd' at line 54, column 50
-------------------------------------------------
52: for (i in 1:n) {
53: if (censored[i]) {
54: target += gpareto_lcdf(value[i] | k, sigma);
^
55: } else {
-------------------------------------------------
Error in stanc(file = file, model_code = model_code, model_name = model_name, :
failed to parse Stan model 'gpd' due to the above error.
In my R studio version, it seems to be complaining about the sigma parameter and not being able to find a match for it. I don't understand why this is an issue given that sigma is defined in my gpareto_lcdf. Here is the code that I am using:
functions {
real gpareto_lpdf(vector y, real k, real sigma) {
// generalised Pareto log pdf
int N = rows(y);
real inv_k = inv(k);
if (k<0 && max(y)/sigma > -inv_k)
reject("k<0 and max(y)/sigma > -1/k; found k, sigma =", k, sigma)
if (sigma<=0)
reject("sigma<=0; found sigma =", sigma)
if (fabs(k) > 1e-15)
return -(1+inv_k)*sum(log1p((y) * (k/sigma))) -N*log(sigma);
else
return -sum(y)/sigma -N*log(sigma); // limit k->0
}
real gpareto_lcdf(vector y, real k, real sigma) {
// generalised Pareto log cdf
real inv_k = inv(k);
if (k<0 && max(y)/sigma > -inv_k)
reject("k<0 and max(y)/sigma > -1/k; found k, sigma =", k, sigma)
if (sigma<=0)
reject("sigma<=0; found sigma =", sigma)
if (fabs(k) > 1e-15)
return sum(log1m_exp((-inv_k)*(log1p((y) * (k/sigma)))));
else
return sum(log1m_exp(-(y)/sigma)); // limit k->0
}
}
data {
// the input data
int<lower = 1> n;
real<lower = 0> value[n];
int<lower = 0, upper = 1> censored[n];
// parameters for the prior
real<lower = 0> a;
real<lower = 0> b;
}
parameters {
real k;
real sigma;
}
model {
// prior
k ~ gamma(a, b);
sigma ~ gamma(a,b);
// likelihood
for (i in 1:n) {
if (censored[i]) {
target += gpareto_lcdf(value[i] | k, sigma);
} else {
target += gpareto_lpdf(value[i] | k, sigma);
}
}
}
Clearly sigma is defined in the gpareto_lcdf and so I am unsure why Stan is complaining about this.
Your code in the likelihood section of the model block doesn't match the way you have defined the gpareto...() functions in the functions block. The gpareto functions take a vector as the first argument but instead you are looping through and trying to pass a single element of value each time. That's why you get the error that the data types you are passing to gpareto_lcdf() do not match the "signature" of the function. The function expects the first argument to be a vector, the second to be a real, and the third to be a real. But you are passing three reals.
The error has nothing to do with sigma. The ^ symbol is pointing to the entire function call to gpareto_lcdf() and just happens to be pointing near where the word sigma is, but the error isn't related to sigma.
To fix this error, you would need to do one of the following:
Redefine the gpareto() functions to take three real arguments and keep your loop in the model block as is.
Rewrite your model block to not use a loop and instead be vectorized.
I'm not sure the vectorization will work with the condition you have in the model block so you may be forced to go with the first solution.
I would recommend posting this question on the Stan forum where you may get a better answer.
I am having trouble with my Datablock not having show_batch methods when customising to my own use case.
I am trying to port some of my code from fastai v1 to v2. Working through the Datablock tutorial https://docs.fast.ai/tutorial.datablock.html
My Datablock & Dataset:
dblock = DataBlock(get_items = get_image_files,
get_y = parent_label,
splitter = RandomSplitter())
dsets = dblock.datasets("PlantVillage-Dataset/raw/color/")
dsets.train[0] # this works
The error I get when I try dsets.show_batch():
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-56-5a2f74730596> in <module>
----> 1 dsets.show_batch()
~/.pyenv/versions/3.7.8/envs/fastai/lib/python3.7/site-packages/fastai/data/core.py in __getattr__(self, k)
315 return res if is_indexer(it) else list(zip(*res))
316
--> 317 def __getattr__(self,k): return gather_attrs(self, k, 'tls')
318 def __dir__(self): return super().__dir__() + gather_attr_names(self, 'tls')
319 def __len__(self): return len(self.tls[0])
~/.pyenv/versions/3.7.8/envs/fastai/lib/python3.7/site-packages/fastcore/transform.py in gather_attrs(o, k, nm)
163 att = getattr(o,nm)
164 res = [t for t in att.attrgot(k) if t is not None]
--> 165 if not res: raise AttributeError(k)
166 return res[0] if len(res)==1 else L(res)
167
AttributeError: show_batch
dls = dblock.dataloaders(path)
dls.show_batch()
After intialising the Datablock I needed to construct a dataloader for batch construction.
I have written the following custom evaluation function to use with xgboost, in order to optimize F1. Umfortuantely it returns an exception when run with xgboost.
The evaluation function is the following:
def F1_eval(preds, labels):
t = np.arange(0, 1, 0.005)
f = np.repeat(0, 200)
Results = np.vstack([t, f]).T
P = sum(labels == 1)
for i in range(200):
m = (preds >= Results[i, 0])
TP = sum(labels[m] == 1)
FP = sum(labels[m] == 0)
if (FP + TP) > 0:
Precision = TP/(FP + TP)
Recall = TP/P
if (Precision + Recall >0) :
F1 = 2 * Precision * Recall / (Precision + Recall)
else:
F1 = 0
Results[i, 1] = F1
return(max(Results[:, 1]))
Below I provide a reproducible example along with the error message:
from sklearn import datasets
Wine = datasets.load_wine()
X_wine = Wine.data
y_wine = Wine.target
y_wine[y_wine == 2] = 1
X_wine_train, X_wine_test, y_wine_train, y_wine_test = train_test_split(X_wine, y_wine, test_size = 0.2)
clf_wine = xgb.XGBClassifier(max_depth=6, learning_rate=0.1,silent=False, objective='binary:logistic', \
booster='gbtree', n_jobs=8, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, \
subsample=0.8, colsample_bytree=0.8, colsample_bylevel=1, reg_alpha=0, reg_lambda=1)
clf_wine.fit(X_wine_train, y_wine_train,\
eval_set=[(X_wine_train, y_wine_train), (X_wine_test, y_wine_test)], eval_metric=F1_eval, early_stopping_rounds=10, verbose=True)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-453-452852658dd8> in <module>()
12 clf_wine = xgb.XGBClassifier(max_depth=6, learning_rate=0.1,silent=False, objective='binary:logistic', booster='gbtree', n_jobs=8, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=0.8, colsample_bylevel=1, reg_alpha=0, reg_lambda=1)
13
---> 14 clf_wine.fit(X_wine_train, y_wine_train,eval_set=[(X_wine_train, y_wine_train), (X_wine_test, y_wine_test)], eval_metric=F1_eval, early_stopping_rounds=10, verbose=True)
15
C:\ProgramData\Anaconda3\lib\site-packages\xgboost\sklearn.py in fit(self, X, y, sample_weight, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set)
519 early_stopping_rounds=early_stopping_rounds,
520 evals_result=evals_result, obj=obj, feval=feval,
--> 521 verbose_eval=verbose, xgb_model=None)
522
523 self.objective = xgb_options["objective"]
C:\ProgramData\Anaconda3\lib\site-packages\xgboost\training.py in train(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, learning_rates)
202 evals=evals,
203 obj=obj, feval=feval,
--> 204 xgb_model=xgb_model, callbacks=callbacks)
205
206
C:\ProgramData\Anaconda3\lib\site-packages\xgboost\training.py in _train_internal(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)
82 # check evaluation result.
83 if len(evals) != 0:
---> 84 bst_eval_set = bst.eval_set(evals, i, feval)
85 if isinstance(bst_eval_set, STRING_TYPES):
86 msg = bst_eval_set
C:\ProgramData\Anaconda3\lib\site-packages\xgboost\core.py in eval_set(self, evals, iteration, feval)
957 if feval is not None:
958 for dmat, evname in evals:
--> 959 feval_ret = feval(self.predict(dmat), dmat)
960 if isinstance(feval_ret, list):
961 for name, val in feval_ret:
<ipython-input-383-dfb8d5181b18> in F1_eval(preds, labels)
11
12
---> 13 P = sum(labels == 1)
14
15
TypeError: 'bool' object is not iterable
I do not understand why the function is not working. I have followed the examples here: https://github.com/dmlc/xgboost/blob/master/demo/guide-python/custom_objective.py
I would like to understand where I err.
When doing sum(labels == 1), Python evaluates labels == 1 as a Boolean object, thus you get TypeError: 'bool' object is not iterable
The function sum expecting an iterable object, like a list. Here's an example of your error:
In[32]: sum(True)
Traceback (most recent call last):
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2963, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-32-6eb8f80b7f2e>", line 1, in <module>
sum(True)
TypeError: 'bool' object is not iterable
If you want to use f1_score of scikit-learn you can implement the following wrapup:
from sklearn.metrics import f1_score
import numpy as np
def f1_eval(y_pred, dtrain):
y_true = dtrain.get_label()
err = 1-f1_score(y_true, np.round(y_pred))
return 'f1_err', err
params of the wrap up are list (of predictions) and DMatrix, and it returns a string, float
# Setting your classifier
clf_wine = xgb.XGBClassifier(max_depth=6, learning_rate=0.1,silent=False, objective='binary:logistic', \
booster='gbtree', n_jobs=8, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, \
subsample=0.8, colsample_bytree=0.8, colsample_bylevel=1, reg_alpha=0, reg_lambda=1)
# When you fit, add eval_metric=f1_eval
# Please don't forget to insert all the .fit arguments required
clf_wine.fit(eval_metric=f1_eval)
Here you can see an example of how to implement custom objective function and custom evaluation metric
Example containing the following code:
# user defined evaluation function, return a pair metric_name, result
# NOTE: when you do customized loss function, the default prediction value is margin
# this may make builtin evaluation metric not function properly
# for example, we are doing logistic loss, the prediction is score before logistic transformation
# the builtin evaluation error assumes input is after logistic transformation
# Take this in mind when you use the customization, and maybe you need write customized evaluation function
def evalerror(preds, dtrain):
labels = dtrain.get_label()
# return a pair metric_name, result
# since preds are margin(before logistic transformation, cutoff at 0)
return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
which specify that an evaluation function gets as arguments (predictions, dtrain) dtrain is of type DMatrix and returns a string, float which is the name of the metric and the error.
Adding working python code example
import numpy as np
def _F1_eval(preds, labels):
t = np.arange(0, 1, 0.005)
f = np.repeat(0, 200)
results = np.vstack([t, f]).T
# assuming labels only containing 0's and 1's
n_pos_examples = sum(labels)
if n_pos_examples == 0:
raise ValueError("labels not containing positive examples")
for i in range(200):
pred_indexes = (preds >= results[i, 0])
TP = sum(labels[pred_indexes])
FP = len(labels[pred_indexes]) - TP
precision = 0
recall = TP / n_pos_examples
if (FP + TP) > 0:
precision = TP / (FP + TP)
if (precision + recall > 0):
F1 = 2 * precision * recall / (precision + recall)
else:
F1 = 0
results[i, 1] = F1
return (max(results[:, 1]))
if __name__ == '__main__':
labels = np.random.binomial(1, 0.75, 100)
preds = np.random.random_sample(100)
print(_F1_eval(preds, labels))
And if you want to implement _F1_eval to work specifically for xgboost evaluation methods add this:
def F1_eval(preds, dtrain):
res = _F1_eval(preds, dtrain.get_label())
return 'f1_err', 1-res
I am a noob trying to build a network to classify 2 sequences of floats to one of 16450 different integers. I have 70408 samples and I have padded each sample to have 1400 values. So 1 sample has 2 column vectors eg. [104.243,120.12...], [125.25,14.556...]. Both my x_train is size (70408,1400). I am trying to use keras' functional API but can't seem to figure out the right input shape. Any help would be appreciated.
samples = 70408
mass_size = 1400
intensity_size = 1400
output_size = 16450
mass_input = Input(shape=(samples,mass_size), dtype='float32')
mass_net = layers.Conv1D(32,5,activation='relu')(mass_input)
mass_net = layers.AveragePooling1D(3)(mass_net)
mass_net = layers.Conv1D(16,5,activation='relu')(mass_net)
mass_net = layers.GlobalAveragePooling1D()(mass_net)
intensity_input = Input(shape=(samples,intensity_size), dtype='float32')
intensity_net = layers.Conv1D(32,5,activation='relu')(intensity_input)
intensity_net = layers.AveragePooling1D(3)(intensity_net)
intensity_net = layers.Conv1D(16,5,activation='relu')(intensity_net)
intensity_net = layers.GlobalAveragePooling1D()(intensity_net)
concatenated = layers.concatenate([mass_net,intensity_net],axis=-1)
output = layers.Dense(output_size,activation='softmax')(concatenated)
print(mass_data.shape, intensity_data.shape)
model = Model([mass_data,intensity_data],output)
model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['acc'])
model.fit([mass_data,intensity_data],y_train,epochs=10,batch_size=128)
The error I keep getting is:
TypeError Traceback (most recent call last)
<ipython-input-18-aab93c439dd0> in <module>()
28
29 print(mass_data.shape, intensity_data.shape)
---> 30 model = Model([mass_data,intensity_data],output)
31 model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['acc'])
32
~\Anaconda3\envs\deeplearning\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name +
90 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~\Anaconda3\envs\deeplearning\lib\site-packages\keras\engine\topology.py in __init__(self, inputs, outputs, name)
1528
1529 # Check for redundancy in inputs.
-> 1530 if len(set(self.inputs)) != len(self.inputs):
1531 raise ValueError('The list of inputs passed to the model '
1532 'is redundant. '
TypeError: unhashable type: 'numpy.ndarray'
The problem seems to be here:
model = Model([mass_data,intensity_data],output)
You should use the input tensors you created, not numpy data:
model = Model([mass_input, intensity_input],output)
Another problem, related to my old comment is the input_shape.
Since you now have your data as (samples, length, features), you need input_shape=(length,features)
I'm a beginner with TF
I've tried to adapt a code which is working well with some other data (noMNIST) to some new data, and i have a dimensionality error, and i don't know how to deal with it.
To debug, i'm trying to use tf.shape method but it doesn't give me the info i need...
def reformat(dataset, labels):
#dataset = dataset.reshape((-1, num_var)).astype(np.float32)
# Map 2 to [0.0, 1.0, 0.0 ...], 3 to [0.0, 0.0, 1.0 ...]
labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)
return dataset, labels
train_dataset, train_labels = reformat(train_dataset, train_labels)
valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)
test_dataset, test_labels = reformat(test_dataset, test_labels)
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
type(train_dataset)
Training set (790184, 29) (790184, 39) Validation set (43899, 29)
(43899, 39) Test set (43899, 29) (43899, 39)
# Adding regularization to the 1 hidden layer network
graph1 = tf.Graph()
batch_size = 128
num_steps=3001
import datetime
startTime = datetime.datetime.now()
def define_and_run_batch(beta):
num_RELU =1024
with graph1.as_default():
# Input data. For the training data, we use a placeholder that will be fed
# at run time with a training minibatch.
tf_train_dataset = tf.placeholder(tf.float32,
shape=(batch_size, num_var))
tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
# Variables.
weights_RELU = tf.Variable(
tf.truncated_normal([num_var, num_RELU]))
print(tf.shape(weights_RELU) )
biases_RELU = tf.Variable(tf.zeros([num_RELU]))
weights_layer1 = tf.Variable(
tf.truncated_normal([num_RELU, num_labels]))
biases_layer1 = tf.Variable(tf.zeros([num_labels]))
# Training computation.
logits_RELU = tf.matmul(tf_train_dataset, weights_RELU) + biases_RELU
RELU_vec = tf.nn.relu(logits_RELU)
logits_layer = tf.matmul(RELU_vec, weights_layer1) + biases_layer1
# loss = tf.reduce_mean(
# tf.nn.softmax_cross_entropy_with_logits(logits_layer, tf_train_labels))
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits_layer, tf_train_labels,name="cross_entropy")
l2reg = tf.reduce_sum(tf.square(weights_RELU))+tf.reduce_sum(tf.square(weights_layer1))
# beta = 0.005
loss = tf.reduce_mean(cross_entropy+beta*l2reg)
# Optimizer.
optimizer = tf.train.GradientDescentOptimizer(0.3).minimize(loss)
# Predictions for the training, validation, and test data.
train_prediction = tf.nn.softmax(logits_layer)
print("ok")
print(tf.shape(weights_RELU) )
valid_prediction = tf.nn.softmax(
tf.matmul(tf.nn.relu((tf.matmul(tf_valid_dataset, weights_RELU) + biases_RELU)),weights_layer1)+biases_layer1)
test_prediction =tf.nn.softmax(
tf.matmul(tf.nn.relu((tf.matmul(tf_test_dataset, weights_RELU) + biases_RELU)),weights_layer1)+biases_layer1)
with tf.Session(graph=graph1) as session:
tf.initialize_all_variables().run()
print("Initialized")
for step in range(num_steps):
# Pick an offset within the training data, which has been randomized.
# Note: we could use better randomization across epochs.
offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
# Generate a minibatch.
batch_data = train_dataset[offset:(offset + batch_size), :]
batch_labels = train_labels[offset:(offset + batch_size), :]
# Prepare a dictionary telling the session where to feed the minibatch.
# The key of the dictionary is the placeholder node of the graph to be fed,
# and the value is the numpy array to feed to it.
feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
#
_, l, predictions, logits = session.run(
[optimizer, loss,train_prediction,logits_RELU], feed_dict=feed_dict)
if (step % 500 == 0):
print("Minibatch loss at step %d: %f" % (step, l))
print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))
print("Validation accuracy: %.1f%%" % accuracy(
valid_prediction.eval(), valid_labels))
test_acc = accuracy(test_prediction.eval(), test_labels)
print("Test accuracy: %.1f%%" % test_acc)
print('loss=%s' % l)
x = datetime.datetime.now() - startTime
print(x)
return(test_acc,round(l,5))
define_and_run_batch(0.005)
Tensor("Shape:0", shape=(2,), dtype=int32) ok Tensor("Shape_1:0",
shape=(2,), dtype=int32)
--------------------------------------------------------------------------- ValueError Traceback (most recent call
last) in ()
94 return(test_acc,round(l,5))
95
---> 96 define_and_run_batch(0.005)
in define_and_run_batch(beta)
54 print(tf.shape(weights_RELU) )
55 valid_prediction = tf.nn.softmax(
---> 56 tf.matmul(tf.nn.relu((tf.matmul(tf_valid_dataset, weights_RELU) + biases_RELU)),weights_layer1)+biases_layer1)
57
58
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.pyc
in matmul(a, b, transpose_a, transpose_b, a_is_sparse, b_is_sparse,
name)
949 transpose_a=transpose_a,
950 transpose_b=transpose_b,
--> 951 name=name)
952
953 sparse_matmul = gen_math_ops._sparse_mat_mul
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.pyc in _mat_mul(a, b, transpose_a, transpose_b, name)
684 """
685 return _op_def_lib.apply_op("MatMul", a=a, b=b, transpose_a=transpose_a,
--> 686 transpose_b=transpose_b, name=name)
687
688
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.pyc
in apply_op(self, op_type_name, name, **keywords)
653 op = g.create_op(op_type_name, inputs, output_types, name=scope,
654 input_types=input_types, attrs=attr_protos,
--> 655 op_def=op_def)
656 outputs = op.outputs
657 return _Restructure(ops.convert_n_to_tensor(outputs), output_structure)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc
in create_op(self, op_type, inputs, dtypes, input_types, name, attrs,
op_def, compute_shapes, compute_device) 2040
original_op=self._default_original_op, op_def=op_def) 2041 if
compute_shapes:
-> 2042 set_shapes_for_outputs(ret) 2043 self._add_op(ret) 2044
self._record_op_seen_by_control_dependencies(ret)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc
in set_shapes_for_outputs(op) 1526 raise RuntimeError("No
shape function registered for standard op: %s" 1527
% op.type)
-> 1528 shapes = shape_func(op) 1529 if len(op.outputs) != len(shapes): 1530 raise RuntimeError(
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/ops/common_shapes.pyc
in matmul_shape(op)
87 inner_a = a_shape[0] if transpose_a else a_shape[1]
88 inner_b = b_shape[1] if transpose_b else b_shape[0]
---> 89 inner_a.assert_is_compatible_with(inner_b)
90 return [tensor_shape.TensorShape([output_rows, output_cols])]
91
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/framework/tensor_shape.pyc
in assert_is_compatible_with(self, other)
92 if not self.is_compatible_with(other):
93 raise ValueError("Dimensions %s and %s are not compatible"
---> 94 % (self, other))
95
96 def merge_with(self, other):
ValueError: Dimensions Dimension(29) and Dimension(30) are not
compatible
the whole code is on my github
https://github.com/FaguiCurtain/Kaggle-SF
the Udacity Assignment 3 file is working
the original data is here
https://www.kaggle.com/c/sf-crime/data
in Udacity, the data were images and each image was a 28x28 matrix which was reformatted into flattened vectors of size 784
in the Kaggle-SF file, i am feeding vectors of size 29, and labels can take 39 different values.
thanks for your help
In debug mode you can check shapes of you Tensors.
by the way you error is valid_prediction assignment. to make it better for debugging and reading it's better to define each step in a separate line. you are using 4 operation in 1 line. BTW in debug mode (for example in Pycharm) you can inspect the element and check what is causing the problem
To check the dimensions, you can directly print the tensors. When you print the tensors, you can view the dimensions. I suggest if you are a beginner, try using the 'tf.layers' package which contains high level wrappers for the various layers one would need to build a CNN in tensorflow. By using this, you can avoid having to deal with the various low level operations like 'matmul' and adding bias for example. The activations can also be directly applied by the layers without having to implement it manually.
As far as debugging is concerned, from the code since you have merged the operations, its hard to see what is going on under the hood unless we can use a proper debugger. If you are not using an IDE, I suggest using 'pudb'.