I'm trying to get a multilabel model going in tensorflow. I saw a related question here: Multiple labels with tensorflow, but couldn't get the solution working.
The code is from a tensorflow tutorial. https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/input_fn/boston.py
FEATURES = ["crim", "zn", "indus", "nox", "rm",
"dis", "tax", "ptratio"]
LABELS = ["medv", "age"]
def get_input_fn(data_set, num_epochs=None, shuffle=True):
return tf.estimator.inputs.pandas_input_fn(
x=pd.DataFrame({k: data_set[k].values for k in FEATURES}),
# y=pd.Series(data_set[LABEL].values),
y=list(map(lambda label: data_set[label].values, LABELS)),
num_epochs=num_epochs,
shuffle=shuffle)
In my regression I set the label dimension to 2.
regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols,
label_dimension=2,
hidden_units=[10, 10],
model_dir="/tmp/boston_model")
With my try I get:
Traceback (most recent call last):
File "./boston.py", line 85, in <module>
tf.app.run()
File "/home/jillian/.eb/software/machine-learning/1.00/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "./boston.py", line 67, in main
regressor.train(input_fn=get_input_fn(training_set), steps=5000)
File "./boston.py", line 43, in get_input_fn
shuffle=shuffle)
File "/home/jillian/.eb/software/machine-learning/1.00/lib/python3.6/site-packages/tensorflow/python/estimator/inputs/pandas_io.py", line 87, in pand
as_input_fn
'Index for y: %s\n' % (x.index, y.index))
ValueError: Index for x and y are mismatched.
Index for x: RangeIndex(start=0, stop=400, step=1)
Index for y: <built-in method index of list object at 0x7f6f64a5bb48>
I also tried setting y to a numpy array instead of a list.
Related
I'm new to Python and TensorFlow and I'm trying to build a simple working example with fake data in TensorFlow. My goal is to use the DNNRegressor estimator to predict a real value from a multidimensional input. This is the code I wrote:
import pandas as pd
import tensorflow as tf
import numpy as np
# Amount of train samples
m_train = 1000
# Amount of test samples
m_test = 100
# Dimensions for each sample
n = 10
def from_dataset(ds):
return lambda: ds.make_one_shot_iterator().get_next()
# Create random samples with numpy
train_data = (np.random.sample((m_train,n)), np.random.sample((m_train,1)))
test_data = (np.random.sample((m_test,n)), np.random.sample((m_test,1)))
# Create two datasets, one for trainning and the other for testing
train_dataset = tf.data.Dataset.from_tensor_slices(train_data)
test_dataset = tf.data.Dataset.from_tensor_slices(test_data)
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=n)]
model = tf.estimator.DNNRegressor(hidden_units=[20, 20], feature_columns=feature_columns)
# Train the model
model.train(input_fn=from_dataset(train_dataset), steps=1000)
# Evaluate the unseen samples
eval_result = model.evaluate(input_fn=from_dataset(test_dataset))
And this is the error I get:
$ python fake.py
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp1j5irF
Traceback (most recent call last):
File "fake.py", line 28, in <module>
model.train(input_fn=from_dataset(train_dataset), steps=1000)
File "/usr/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 314, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/usr/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 743, in _train_model
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "/usr/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 725, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/usr/lib/python2.7/site-packages/tensorflow/python/estimator/canned/dnn.py", line 448, in _model_fn
config=config)
File "/usr/lib/python2.7/site-packages/tensorflow/python/estimator/canned/dnn.py", line 153, in _dnn_model_fn
'Given type: {}'.format(type(features)))
ValueError: features should be a dictionary of `Tensor`s. Given type: <class 'tensorflow.python.framework.ops.Tensor'>
I supose I have to use a dictionary of Tensors, but I'm just beginning in Python and I don't know how to do it.
You need to return the iterator returned by get_one(), rather than a lambda function that returns the iterator. Check out https://github.com/tensorflow/tensorflow/blob/r1.8/tensorflow/examples/get_started/regression/dnn_regression.py
I'm trying to expand the tflearn example for linear regression by increasing the number of columns to 21.
from trafficdata import X,Y
import tflearn
print(X.shape) #(1054, 21)
print(Y.shape) #(1054,)
# Linear Regression graph
input_ = tflearn.input_data(shape=[None,21])
linear = tflearn.single_unit(input_)
regression = tflearn.regression(linear, optimizer='sgd', loss='mean_square',
metric='R2', learning_rate=0.01)
m = tflearn.DNN(regression)
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
print("\nRegression result:")
print("Y = " + str(m.get_weights(linear.W)) +
"*X + " + str(m.get_weights(linear.b)))
However, tflearn complains:
Traceback (most recent call last):
File "linearregression.py", line 16, in <module>
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
File "/usr/local/lib/python3.5/dist-packages/tflearn/models/dnn.py", line 216, in fit
callbacks=callbacks)
File "/usr/local/lib/python3.5/dist-packages/tflearn/helpers/trainer.py", line 339, in fit
show_metric)
File "/usr/local/lib/python3.5/dist-packages/tflearn/helpers/trainer.py", line 818, in _train
feed_batch)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 975, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (64,) for Tensor 'TargetsData/Y:0', which has shape '(21,)'
I found the shape (64, ) comes from the default batch size of tflearn.regression().
Do I need to transform the labels (Y)? In what way?
Thanks!
I tried to do the same. I made these changes to get it to work
# linear = tflearn.single_unit(input_)
linear = tflearn.fully_connected(input_, 1, activation='linear')
My guess is that with features >1 you cannot use tflearn.single_unit(). You can add additional fully_connected layers, but the last one must have only 1 neuron because Y.shape=(?,1)
You have 21 features. Therefore, you cannot use linear = tflearn.single_unit(input_)
Instead try this: linear = tflearn.fully_connected(input_, 21, activation='linear')
The error you get is because your labels, i.e., Y has a shape of (1054,).
You have to first preprocess it.
Try using the code given below before # linear regression graph:
Y = np.expand_dims(Y,-1)
Now before regression = tflearn.regression(linear, optimizer='sgd', loss='mean_square',metric='R2', learning_rate=0.01), type the below code:
linear = tflearn.fully_connected(linear, 1, activation='linear')
I have been trying to implement gradient descent with respect to complex parameter/weights in TensorFlow, but I receive the following error message:
Traceback (most recent call last):
File "/home/reg/complex_gradients.py", line 15, in <module>
gate_gradients=optimizer.GATE_NONE)
File "/usr/local/python3/lib/python3.5/site-packages/tensorflow/python/training/optimizer.py", line 196, in minimize
grad_loss=grad_loss)
File "/usr/local/python3/lib/python3.5/site-packages/tensorflow/python/training/optimizer.py", line 257, in compute_gradients
self._assert_valid_dtypes([v for g, v in grads_and_vars if g is not None])
File "/usr/local/python3/lib/python3.5/site-packages/tensorflow/python/training/optimizer.py", line 379, in _assert_valid_dtypes
dtype, t.name, [v for v in valid_dtypes]))
ValueError: Invalid type tf.complex64 for w:0, expected: [tf.float32, tf.float64, tf.float16].
I boiled down my code to this to recreate the error:
import tensorflow as tf
x = tf.placeholder(dtype=tf.complex64)
y = tf.placeholder(dtype=tf.float32)
initial_values = tf.complex(real=tf.random_uniform([100, 100]), imag=tf.random_uniform([100, 100]))
weigths = tf.Variable(initial_values, name='w', dtype=tf.complex64)
loss = tf.nn.l2_loss(tf.complex_abs(tf.matmul(x, weigths)) - y, name='loss')
optimizer = tf.train.GradientDescentOptimizer(0.001)
train = optimizer.minimize(loss,
global_step=0.001,
gate_gradients=optimizer.GATE_NONE)
What did I miss? Does Tensorflow simply not support complex numbers? Any suggestions to make it work anyway? Would be thankful for any help.
I've been playing with the Tensorflow library doing the tutorials.
I'm using this example. And I changed the parameters in the example from this: n_classes = 15
to this: n_classes = 2 as I have only two classes to classify.
I read data like:
train = pandas.read_csv('tensorflow_feed/test/train_with_abs.csv', header=None)
X_train, y_train = train[1], train[0]
test = pandas.read_csv('tensorflow_feed/test/test_with_abs.csv', header=None)
X_test, y_test = test[1], test[0]
But it gives following error:
Total words: 35
Traceback (most recent call last):
File "/home/sumit/PycharmProjects/experiments/text_classification_save_restore.py", line 94, in <module>
classifier.fit(X_train, y_train)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/base.py", line 160, in fit
monitors=monitors)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 449, in _train_model
train_op, loss_op = self._get_train_ops(features, targets)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 673, in _get_train_ops
_, loss, train_op = self._call_model_fn(features, targets, ModeKeys.TRAIN)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 656, in _call_model_fn
features, targets, mode=mode)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/base.py", line 369, in _model_fn
predictions, loss = model_fn(features, targets)
File "/home/sumit/PycharmProjects/experiments/text_classification_save_restore.py", line 73, in rnn_model
word_list = tf.unpack(word_vectors, axis=1)
TypeError: unpack() got an unexpected keyword argument 'axis'
Process finished with exit code 1
The "axis" parameter was just added to tf.unpack on June 23, and the example you're looking at was changed to use it:
https://github.com/tensorflow/tensorflow/commit/eff93149a6dc8e6826898fd9f9c28c81e21c9836
So I suggest either:
use an older version of the example from before that commit, e.g.:
https://github.com/tensorflow/tensorflow/blob/892ca4ddc12852a7b4633fd08f163941356cb4e6/tensorflow/examples/skflow/text_classification_save_restore.py
build a newer Tensorflow from github HEAD.
I hope that helps!
I wrote a code for training a particular data set using SVM using Opencv in Python(this is only part of the total code).
svm_params = dict( kernel_type = cv2.ml.SVM_LINEAR ,svm_type = cv2.ml.SVM_C_SVC,C=2.67, gamma=3 )
svm = cv2.ml.SVM_create()
trainingData,labels = getTrainingData()
svm.train(trainingData, labels, params=svm_params)
svm.save('svm_data.dat')
trainingData and labels are lists of features and responses respectively.
When I tried to run the code, I got an error which says:
Traceback (most recent call last):
File "C:\Python27\Python\fc.py", line 156, in <module>
svm.train(trainingData, labels, params=svm_params)
TypeError: an integer is required
Then I tried to convert lists of trainingData, Labels into numpy arrays.
This is the code when I converted them to numpy arrays:
svm_params = dict( kernel_type = cv2.ml.SVM_LINEAR ,svm_type = cv2.ml.SVM_C_SVC,C=2.67, gamma=3 )
svm = cv2.ml.SVM_create()
trainingData,labels = getTrainingData()
trainData = np.asarray(trainingData)
responses = np.asarray(labels)
svm.train(trainData, responses, params=svm_params)
svm.save('svm_data.dat')
Then it gave some other error:
Traceback (most recent call last):
File "C:\Python27\Python\fc.py", line 155, in <module>
svm.train(trainData, responses, params=svm_params)
TypeError: only length-1 arrays can be converted to Python scalars
trainingData and labels are features and their corresponding responses. I tried to print trainingData and labels: First few rows of trainingData are:
[0, 300, 866, 214, 120, 38] [[0, 140, 1620, 276, 162, 18], [0, 111, 1085, 207, 132, 12], [0, 102, 570, 174, 102, 10],..
Labels:
[[1], [1], [1], [1], [1], [1],...
What changes should I make for my code to successfully train the data?