After some work, the question becomes: how to feed a placeholder tensor without fixed shape? The code is as follows. Note that tensor 'x' has shape [?, 32, 32, 64]. Obviously, the shape of 'ini_obj' is wrong. So, how can I feed tensor 'x' in 'eval()' method.
ini_obj = np.random.random((None, 32, 32, 64)) # numpy array
sess = tf.Session()
sess.run(x)
x.eval(feed_dict={x: ini_obj}, session=sess)
To convert a tensor to numpy array, you have to run eval() function in keras.
Related
I am using VGG16 for transfer learning. My images are grayscale. So, I need to change the input channel shape of Vgg16 from (224, 224, 3) to (224, 224, 1). I tried the following code and got error:
TypeError: build() takes from 1 to 2 positional arguments but 4 were given
Can anyone help me where Am I doing it wrong?
vgg16_model= load_model('Fetched_VGG.h5')
vgg16_model.summary()
# transform the model to Sequential
model= Sequential()
for layer in vgg16_model.layers[1:-1]:
model.add(layer)
# Freezing the layers (Oppose weights to be updated)
for layer in model.layers:
layer.trainable = False
model.build(224,224,1)
model.add(Dense(2, activation='softmax', name='predictions'))
you can't, even if you get rid of the input layer, this model has a graph that has already been compiled and your first conv layer expects an input with 3 channels. I don't think there is really an easy work around to make it accept 1 channel if there is any at all.
you need to repeat your data in third dimension and have the same grayscale image in all 3 bands instead of RGB, that works just fine.
if your image has the shape of : (224,224,1):
import numpy as np
gray_image_3band = np.repeat(gray_img, repeats = 3, axis = -1)
if your image has the shape of : (224,224)
gray_image_3band = np.repeat(gray_img[..., np.newaxis], repeats = 3, axis = -1)
you don't need to call the model.build() anymore this way, keep the input layer. but if you ever wanted to call it you need to pass the shape as a tuple like this:
model.build( (224, 224, 1) ) # this is correct, notice the parentheses
This seems like a trivial question, but I've been unable to find the answer.
I have batched sequences of images of shape:
[batch_size, number_of_frames, frame_height, frame_width, number_of_channels]
and I would like to pass each frame through a few convolutional and pooling layers. However, TensorFlow's conv2d layer accepts 4D inputs of shape:
[batch_size, frame_height, frame_width, number_of_channels]
My first attempt was to use tf.map_fn over axis=1, but I discovered that this function does not propagate gradients.
My second attempt was to use tf.unstack over the first dimension and then use tf.while_loop. However, my batch_size and number_of_frames are dynamically determined (i.e. both are None), and tf.unstack raises {ValueError} Cannot infer num from shape (?, ?, 30, 30, 3) if num is unspecified. I tried specifying num=tf.shape(self.observations)[1], but this raises {TypeError} Expected int for argument 'num' not <tf.Tensor 'A2C/infer/strided_slice:0' shape=() dtype=int32>.
Since all the images (num_of_frames) are passed to the same convolutional model, you can stack both batch and frames together and do the normal convolution. Can be achieved by just using tf.resize as shown below:
# input with size [batch_size, frame_height, frame_width, number_of_channels
x = tf.placeholder(tf.float32,[None, None,32,32,3])
# reshape for the conv input
x_reshapped = tf.reshape(x,[-1, 32, 32, 3])
x_reshapped output size will be (50, 32, 32, 3)
# define your conv network
y = tf.layers.conv2d(x_reshapped,5,kernel_size=(3,3),padding='SAME')
#(50, 32, 32, 3)
#Get back the input shape
out = tf.reshape(x,[-1, tf.shape(x)[1], 32, 32, 3])
The output size would be same as the input: (10, 5, 32, 32, 3
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(sess.run(out, {x:np.random.normal(size=(10,5,32,32,3))}).shape)
#(10, 5, 32, 32, 3)
I am trying to create a variable and then trying to assign it with the value of my convolution layer.
However it is refusing because it is saying shapes are not equal even though I have passed validate_shape=False while creating the variable.
The convolution shape is [32,20,20,3]. How do I pass this into the variable?
the bottom code:
conv = tf.layers.conv2d_transpose(conv, filters=3, kernel_size=3, strides=(2,2), padding='same',activation=tf.nn.relu) # TO ASSIGN LATER
g=tf.Variable(([32,20,20]),dtype=tf.float32,validate_shape=False)#THE VARIABLE
loss = tf.reduce_mean(tf.square(conv))
opt = tf.train.AdamOptimizer().minimize(loss)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(1000):
_, xx,inp,output,target = sess.run([opt, loss,x,conv,y])#
print(xx)
print("subtraction result:",output[0]-target[0])
g=g.assign(conv)
print(g.eval())
I am getting this error:
InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [3] rhs shape= [32,20,20,3]
[[Node: Assign_7 = Assign[T=DT_FLOAT, use_locking=false, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Variable_9, conv2d_transpose_98/Relu)]]
Can someone please help fix this?
I think you want:
import numpy as np
import tensorflow as tf
g = tf.Variable(initial_value=np.zeros((32,20,20,3)), expected_shape=(32,20,20,3), dtype=tf.float32)
If you print g you get the correct shape now:
<tf.Variable 'Variable_3:0' shape=(32, 20, 20, 3) dtype=float32_ref>
What you did was this:
g = tf.Variable(initial_value=(32,20,20), dtype=tf.float32, valid_shape=False)
By not stating expected_shape you defaulted to positional arguments, the first argument of tf.Variable is initial_value as per the documentation below:
__init__(
initial_value=None,
trainable=True,
collections=None,
validate_shape=True,
caching_device=None,
name=None,
variable_def=None,
dtype=None,
expected_shape=None,
import_scope=None,
constraint=None
)
That shape of the initial_value you declared would have been a vector of length [3] which is exactly the shape that the assign operation is complaining about.
Moral of the story: it's generally less buggy to declare arguments by name if you can. :)
again I have some issue with Tensorflow. I am using a FCN model and need to apply a random crop due to memory usage.
tf.random_crop(combined, size=[512, 512, 4])
unfortunately now the new size "sticks" to the tensor and I can not get rid of it.
The issue caused by this is, that the resulting model only accepts input of size 512x512, which cannot be worked around in a nice way, as far as I know.
Is there any solution to either remove the shape information caused by random_crop or to easily adapt the size afterwards after obtaining a trained model?
Thank you in advance.
I don't know if it will completely suit your use-case, but the size parameter of tf.random_crop() can be a tensor, so you can for instance use a placeholder as shown in the example below.
import tensorflow as tf
import numpy as np
image = tf.placeholder(tf.float64, [None, None, 4])
cropped_size = tf.placeholder(tf.int32, [2])
cropped_image = tf.random_crop(image, size=[cropped_size[0], cropped_size[1], 4])
print(cropped_image.get_shape().as_list())
# [None, None, 4]
with tf.Session() as sess:
res = sess.run(cropped_image,
feed_dict={image: np.random.rand(900, 600, 4), cropped_size: [512, 512]})
print(res.shape)
# (512, 512, 4)
EDIT:
There may be different solutions to have the value of cropped_size assigned without using a feed_dict, depending how the crop dimensions are stored ; e.g. using TF file readers (the values would stay unknown till read).
Another simple hack otherwise: take advantage of tf.placeholder_with_default(default_val, shape) (doc), providing default_val with the crop dimensions acquired anyhow. As tf.placeholder_with_default() value isn't actually assigned until runtime (in case you you want to feed this placeholder with a different value), your dimensions would stay None in the graph:
import tensorflow as tf
image = tf.random_uniform((900, 600, 4)) # image tensor, acquired anyhow e.g. from tf.data
cropped_size_for_this_run = [512, 512] # crop dimensions, acquired anyhow
cropped_size = tf.placeholder_with_default(cropped_size_for_this_run, shape=[2])
cropped_image = tf.random_crop(image, size=[cropped_size[0], cropped_size[1], 4])
print(cropped_image.get_shape().as_list())
# [None, None, 4]
with tf.Session() as sess:
# You can leave cropped_size with its default value assigned at runtime:
res = sess.run(cropped_image)
print(res.shape)
# (512, 512, 4)
# ... or you can specify a new one if you wish so:
res = sess.run(cropped_image, feed_dict={cropped_size: [256, 256]})
print(res.shape)
# (256, 256, 4)
# ... It would switch back to the default value if you don't feed one:
res = sess.run(cropped_image)
print(res.shape)
# (512, 512, 4)
I am coding a Convolutional Neural Network to classify images in TensorFlow but there is a problem:
When I try to feed my NumPy array of flattened images (3 channels with RGB values from 0 to 255) to a tf.estimator.inputs.numpy_input_fn I get the following error:
TypeError: Failed to convert object of type <class 'dict'> to Tensor.
Contents: {'x': <tf.Tensor 'random_shuffle_queue_DequeueMany:1' shape=(8,
196608) dtype=uint8>}. Consider casting elements to a supported type.
My numpy_imput_fn looks like this:
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={'x': train_x},
y=train_y,
batch_size=8,
num_epochs=None,
shuffle=True)
In the documentation for the function it is said that x should be a dict of NumPy array:
x: dict of numpy array object.
Nevermind, for those having the same problem I fixed it. In my model function i had:
input_layer = tf.reshape(features, [-1, 256, 256, 1])
Which raised the type error. To fix it you have to access the 'x' key in the features dictionary:
input_layer = tf.reshape(features['x'], [-1, 256, 256, 1])