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