Checking frozen tensorflow model:
wget https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz
I see that input size is Tensor 'input:0', which has shape '(1, 299, 299, 3)', I wonder is it possible to make input (None, 299, 299, 3) to make availible batch prediction with batch_size > 1?
In the general case it may not be possible to do this, as there could be operations that rely on the first dimension being 1 (e.g. suppose tf.squeeze is used on input:0). However, you can try to replace the input with a placeholder of the desired shape. You can do this with tf.graph_util.import_graph_def. If the operations allow it, then TensorFlow should import the graph adjusting the node shapes accordingly. See the following example:
import tensorflow as tf
# First graph
with tf.Graph().as_default():
x = tf.placeholder(tf.float32, [1, 10, 20], name='Input')
y = tf.square(x, name='Output')
print(y)
# Tensor("Output:0", shape=(1, 10, 20), dtype=float32)
gd = tf.get_default_graph().as_graph_def()
# Second graph
with tf.Graph().as_default():
x = tf.placeholder(tf.float32, [None, 10, 20], name='Input')
y, = tf.graph_util.import_graph_def(gd, input_map={'Input:0': x},
return_elements=['Output:0'], name='')
print(y)
# Tensor("Output:0", shape=(?, 10, 20), dtype=float32)
In the first graph, the Output:0 node has a shape (1, 10, 20), which is inferred from the shape of the Input:0 tensor. However, when I take the graph definition from the first graph and load in the second graph, replacing the Input:0 tensor with a placeholder with undefined first dimension, the shape of Output:0 is updated to (?, 10, 20). If I run the operations in the second graph giving an input value with a first dimension greater than one, it will work as expected, because the graph is correct.
Related
I have the network model_classifier, which is the RCNN/classifier component of Faster-RCNN in the middle of a training loop.
loss_class = model_classifier.train_on_batch([X_batch_for_classifier, X2_batch], [Y1_batch, Y2_batch])
Producing the error message:
InvalidArgumentError: Input to reshape is a tensor with 1605632 values, but the requested shape has 100352 [Op:Reshape]
The input tensor shape for model_classifier is (None, 300, 300, 3).
The shapes for the two sets of inputs and two sets of labels are as follows:
X2_for_classifier: (16, 300, 300, 3)
X2_batch: (16, 4, 4)
Y1_batch: (16, 4, 4)
Y2_batch: (16, 4, 24)
I am putting in 16 training examples in in the hope that it will treat all 16 as separate, but it is instead treating all as one training example as a 4D image. I get the same problem regardless of whether I input them as tensors or np.arrays.
I have also tried converting the inputs and targets into a tf.data.Dataset format but that just gives the same problem.
Ok I have fixed it now, the problem was with my network not my data entry.
Where you define the ROIPoolingConv class you have to swap this:
final_output = K.reshape(final_output, (1, self.num_rois, self.pool_size, self.pool_size, self.nb_channels))
for this:
final_output = K.reshape(final_output, (-1, self.num_rois, self.pool_size, self.pool_size, self.nb_channels))
Where the -1 creates a "None" dim to the output tensor to allow for a variable batch size.
I have a neural network with many layers. I have the input to the neural network of dimension [batch_size, 7, 4]. When this input is passed through the network, I observed that only the third dimension of the input keeps changing, that is if my first layer has 20 outputs, then the output of the second layer is [batch_size, 7, 20]. I need the end result after many layers to be of the shape [batchsize, 16].
I have the following questions:
Are the other two dimensions being used at all?
If not, how can I modify my network so that all three dimensions are used?
How do I drop one dimension meaningfully to get the 2-d output that I desire?
Following is my current implementation in Tensorflow v1.14 and Python 3:
out1 = tf.layers.dense(inputs=noisy_data, units=150, activation=tf.nn.tanh) # Outputs [batch, 7, 150]
out2 = tf.layers.dense(inputs=out1, units=75, activation=tf.nn.tanh) # Outputs [batch, 7, 75]
out3 = tf.layers.dense(inputs=out2, units=32, activation=tf.nn.tanh) # Outputs [batch, 7, 32]
out4 = tf.layers.dense(inputs=out3, units=16, activation=tf.nn.tanh) # Outputs [batch, 7, 16]
Any help is appreciated. Thanks.
Answer to Question 1: The data values in 2nd dimension (axis=1) are not being used because if you look at the output of code snippet below (assuming batch_size=2):
>>> input1 = tf.placeholder(float, shape=[2,7,4])
>>> tf.layers.dense(inputs=input1, units=150, activation=tf.nn.tanh)
>>> graph = tf.get_default_graph()
>>> graph.get_collection('variables')
[<tf.Variable 'dense/kernel:0' shape=(4, 150) dtype=float32_ref>, <tf.Variable 'dense/bias:0' shape=(150,) dtype=float32_ref>]
you can see that the dense layer ignores values along 2nd dimension. However, the values along 1st dimension would be considered as it is a part of a batch though the offical tensorflow docs doesn't say anything about the required input shape.
Answer to Question 2: Reshape the input [batch_size, 7, 4] to [batch_size, 28] by using the below line of code before passing the input to the first dense layer:
input1 = tf.reshape(input1, [-1, 7*4])
Answer to Question 3: If you reshape the inputs as above, there is no need to drop a dimension.
I have built a deep CNN based on a research paper and now I am attempting to train it. After all the convolutions and deconvolutions I have performed, I have a result called final.
final = tf.add(add1,add2)
print(final)
Tensor("Add_35:0", shape=(1, 32, 32, 7, 1), dtype=float32)
In my model, I have an image of the size 32x32x7 where each pixel has a corresponding density. The output of the model will be a label for each pixel. Therefore, I have declared two placeholders where "x" represents the input and "y_" represents the output.
x = tf.placeholder(tf.float32, shape=[None, 7168])
y_ = tf.placeholder(tf.float32, shape=[None, 7168])
Now that I am attempting to train the model, I have this line
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=final))
When the model is being trained I receive the error : logits and labels must be same size: logits_size=[7168,1] labels_size=[1,7168]
It makes sense that labels would be this size since this is how I declared it. However, I do not understand why logits is of size [7168,1] when printing out "final" has the shape (1, 32, 32, 7, 1).
Just tf.reshape your final:
final = tf.reshape(final, [None, 7168])
Although I am not sure why it is automatically flattening when you call softmax_cross_entropy_with_logits...
I'm trying to perform a convolution (conv2d) on images of variable dimensions. I have those images in form of an 1-D array and I want to perform a convolution on them, but I have a lot of troubles with the shapes.
This is my code of the conv2d:
tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')
where x is the input image.
The error is:
ValueError: Shape must be rank 4 but is rank 1 for 'Conv2D' (op: 'Conv2D') with input shapes: [1], [5,5,1,32].
I think I might reshape x, but I don't know the right dimensions. When I try this code:
x = tf.reshape(self.x, shape=[-1, 5, 5, 1]) # example
I get this:
ValueError: Dimension size must be evenly divisible by 25 but is 1 for 'Reshape' (op: 'Reshape') with input shapes: [1], [4] and with input tensors computed as partial shapes: input[1] = [?,5,5,1].
You can't use conv2d with a tensor of rank 1. Here's the description from the doc:
Computes a 2-D convolution given 4-D input and filter tensors.
These four dimensions are [batch, height, width, channels] (as Engineero already wrote).
If you don't know the dimensions of the image in advance, tensorflow allows to provide a dynamic shape:
x = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='x')
with tf.Session() as session:
print session.run(x, feed_dict={x: data})
In this example, a 4-D tensor x is created, but only the number of channels is known statically (3), everything else is determined on runtime. So you can pass this x into conv2d, even if the size is dynamic.
But there's another problem. You didn't say your task, but if you're building a convolutional neural network, I'm afraid, you'll need to know the size of the input to determine the size of FC layer after all pooling operations - this size must be static. If this is the case, I think the best solution is actually to scale your inputs to a common size before passing it into a convolutional network.
UPD:
Since it wasn't clear, here's how you can reshape any image into 4-D array.
a = np.zeros([50, 178, 3])
shape = a.shape
print shape # prints (50, 178, 3)
a = a.reshape([1] + list(shape))
print a.shape # prints (1, 50, 178, 3)
I'm using tensorflow run queues to feed my data during training time:
X, Y = tf.train.batch(
[image, label],
batch_size=64
)
However X, Y have forced shape of [64, 32, 32,3], and [64, 10]. During evaluation time I'd like to run loss operation on whole test set, which has dimensions: [10000, 32, 32, 3] and [10000, 10]. I would use feed_dict property in session.run() to overwrite X,Y with my values, however they have incompatible shapes.
Can I somehow instruct tensorflow to forget about first dimension, that is reshape [64, 32, 32, 3] -> [None, 32, 32, 3]? Or is there any other option for me to replace X,Y with another value.
Whole dataset is small enough to fit in memory, therefore I'm using similar approach as in https://github.com/tensorflow/tensorflow/blob/r0.9/tensorflow/examples/how_tos/reading_data/fully_connected_preloaded.py
This is a bit subtle: in TensorFlow terminology, you don't actually want to reshape the tensor (i.e. change the number of elements in each dimension), but instead you want TensorFlow to "forget" a specific dimension, in order to feed values with a range of sizes.
The tf.placeholder_with_default() op is designed to support this case. It takes a default input, which in your case would be the next training batch (of shape [64, ...]); and a shape, which in your case would be the same shape as the input, with the first dimension set to None. You can then feed this placeholder with values of any batch size.
Here's an example of how you'd use it:
X_batch, Y_batch = tf.train.batch([image, label], batch_size=64)
# Alternatively, `X_shape = [None, 32, 32, 3]`
X_shape = tf.TensorShape([None]).concatenate(X_batch.get_shape()[1:])
# Alternatively, `Y_shape = [None, 10]`
Y_shape = tf.TensorShape([None]).concatenate(Y_batch.get_shape()[1:])
# Create tensors that can be fed with a less specific shape
# than `X_batch`, `Y_batch`.
X = tf.placeholder_with_default(X_batch, shape=X_shape)
Y = tf.placeholder_with_default(Y_batch, shape=Y_shape)