I have a Tensorflow pre-trained model for Image Segmentation that receives 6 bands as input, I would like to change the input size of the model to receive 4 bands so I can retrain with my own dataset, but still not able to do it, no sure if this is even possible?
I tried getting the input node by name and change it using import_graph_def with no success, seems like it is asking to respect the dimensions when trying to substitute.
graph = tf.get_default_graph()
tf_new_input = tf.placeholder(shape=(4, 256, 256), dtype='float32', name='new_input')
tf.import_graph_def(graph_def, input_map={"ImageInputLayer": tf_new_input})
But I am getting the following error:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimensions must be equal, but are 4 and 6 for 'import/ImageInputLayer_Sub' (op: 'Sub') with input shapes: [4,256,256], [6,256,256]
You have to convert your 4 channel placeholder input to 6 channel input and also the input image shape should be the same as your 6 channel model expects. You may use any operation but conv2d is an easy operation to perform before you feed it to your existing model. This is how you do it.
with tf.Graph().as_default() as old_graph:
# You have to load your 6 channel input graph here
saver.restore(tf.get_default_session(), <<save_path>>)
# Assuming that input node is named as 'input_node' and
# final node is named as 'softmax_node'
with tf.Graph().as_default() as new_graph:
tf_new_input = tf.placeholder(shape=(None, 256, 256, 4), dtype='float32')
# Map 4 channeled input to 6 channel and
# image input shape should be same as expected by old model.
new_node = tf.nn.conv2d(tf_new_input, (3, 3, 4, 6), strides=1, padding='SAME')
# If you want to obtain output node so that you can further perform operations.
softmax_node = tf.import_graph_def(old_graph, input_map={'input_node:0': new_node},
return_elements=['softmax_node:0'])
user1190882 responded this question very well. Just using this section to post the code for future reference, I had to make a small change by creating the filter in a separate variable since I was getting an error : Shape must be rank 4 but is rank 1 for 'Conv2D' . Also I made a small change since the input format of my model is "Channels First", I added data_format flag.
with tf.Graph().as_default() as new_graph:
tf_new_input = tf.placeholder(shape=(None, 4, 256, 256), dtype='float32')
# Creating separate variable for filter
filterc = tf.Variable(tf.random_normal([3, 3, 4, 6]))
new_node = tf.nn.conv2d(tf_new_input, filterc, strides=1, padding='SAME', data_format='NCHW')
tf.import_graph_def(old_graph, input_map={'ImageInputLayer': new_node})
Related
I was working with manipulating weights of a CNN model (LENET-5) for my academical project where I encountered this problem that it was very difficult to understand what the keras function layer.get_weights() return? My model is
# Create Model LENET-5 Traditional
model = Sequential([
Conv2D(filters=6, kernel_size=(5, 5), activation='tanh', input_shape=(28, 28, 1), padding="same"),
AveragePooling2D(), # pool_size=(2, 2),
Conv2D(filters=16, kernel_size=(5, 5), activation='tanh', input_shape=(10, 10, 1)),
AveragePooling2D(),
Flatten(),
Dense(units=120, activation='tanh'),
Dense(units=84, activation='tanh'),
# Softmax function gives probabilities of each output class
Dense(units=10, activation='softmax')
])
that gives us a summary of:
Now after compiling and training the model, the get_weights() function for a first conv2d layer returns a np array of shape: (5, 5, 1, 6)
Now we know that in first Conv2d layer of LENET-5, there are 6 kernels of 1 channel (grayscale) all containing 5x5 = 25 parameters right? but the array looks like this:
it is 1 array that contains 5 arrays that have 5 more arrays with 1 array of 6 elements. how do I map these to 6 kernels ? which 25 values are from 1st kernel ? it is not understandable because 6 kernels that contains 5x5 weights are present there. Can anyone please elaborate the mapping of these weights to kernels/filters?
To get access of a single weight I need weight[0][0][0][0][0] that returns the first element of first array but I don't know how and which kernel it belongs to. and let's suppose if I want to access weight of 3rd kernel that is on 3rd row and 3rd column how can I access that? there must be an interpretation to that.
I expect someone to please explain me about the shape of weight returned by get_weights() function and how I can map them to kernels of conv2d layer.. Visualization can help a lot too.
I want to get the value of an intermediate tensor in a convolutional neural network for a specific input. I know how to do this in keras and even though I have trained a model using keras, I'm going to move towards constructing and training the model using only tensorflow. Therefore, I want to move away from something like K.function(input_layer, output_layer) which is fairly simple, and instead use tensorflow. I believe I should use placeholder values, like the following approach:
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
loaded_model = tf.keras.models.load_model(filepath)
graph = tf.compat.v1.get_default_graph()
images = tf.compat.v1.placeholder(tf.float32, shape=(None, 28, 28, 1)) # To specify input at MNIST images
output_tensor = graph.get_tensor_by_name(tensor_name) # tensor_name is 'dense_1/MatMul:0'
output = sess.run([output_tensor], feed_dict={images: x_test[0:1]}) # x_test[0:1] is of shape (1, 28, 28, 1)
print(output)
However, I get the following error message for the sess.run() line: Invalid argument: You must feed a value for placeholder tensor 'conv2d_2_input' with dtype float and shape [?,28,28,1]. I am unsure why I get this message because the image used for feed_dict is of type float and is what I believe to be the correct shape. Any help would be suggested.
You must use the input tensor from the Keras model, not make your own new placeholder, which would be disconnected from the rest of the model:
with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
# Load model
loaded_model = tf.keras.models.load_model(filepath)
# Take model input tensor
images = loaded_model.input
# Take output of the second layer (index 1)
output_tensor = loaded_model.layers[1].output
# Evaluate
output = sess.run(output_tensor, feed_dict={images: x_test[0:1]})
print(output)
I'm trying keras.layers.LSTM.
The following code works.
#!/usr/bin/python3
import tensorflow as tf
import numpy as np
from tensorflow import keras
data = np.array([1, 2, 3]).reshape((1, 3, 1))
x = keras.layers.Input(shape=(3, 1))
y = keras.layers.LSTM(10)(x)
model = keras.Model(inputs=x, outputs=y)
print (model.predict(data))
As shown above, the input data shape is (1, 3, 1), and the actual input shape in the Input layer is (3, 1). I'm a little bit confused about this inconsistency of the dimension.
If I use the following shape in the Input layer, it doesn't work:
x = keras.layers.Input(shape=(1, 3, 1))
The error message is as follows:
ValueError: Input 0 of layer lstm is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, 1, 3, 1]
It seems that the rank of the input must be 3, but why should we use a rank-2 shape in the Input layer?
Keras works with "batches" of "samples". Since most models use variable batch sizes that you define only when fitting, for convenience you don't need to care about the batch dimension, but only with the sample dimension.
That said, when you use shape = (3,1), this is the same as defining batch_shape = (None, 3, 1) or batch_input_shape = (None, 3, 1).
The three options mean:
A variable batch size: None
With samples of shape (3, 1).
It's important to know this distinction especially when you are going to create custom layers, losses or metrics. The actual tensors all have the batch dimension and you should take that into account when making operations with tensors.
Check out the documentation for tf.keras.Input. The syntax is as-
tf.keras.Input(
shape=None,
batch_size=None,
name=None,
dtype=None,
sparse=False,
tensor=None,
**kwargs
)
shape: defines the shape of a single sample, with variable batch size.
Notice, that it expects the first value as batch_size otherwise pass batch_size as a parameter explicitly
I want to use a pre-trained BagNet (https://github.com/wielandbrendel/bag-of-local-features-models) to extract features. The net has fixed input height and width, the input is (None,3,224,224). Now, I want to build a new model with fexible input sizes. I tried approaches with model.layers.pop()[0] to remove the first layer and replace it with a flexible input but I get errors:
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input0_6:0", shape=(?, 3, 224, 224), dtype=float32) at layer "input0". The following previous layers were accessed without issue: []
keras_model = bagnets.keras.bagnet8()
keras_model.layers.pop()[0]
x = Input(batch_shape=(None, 3, None, None))
newModel = Model(x, keras_model.output)
How could I ressolve this issue or what are other options?
So recently i am working on a project which i am supposed to take images as input to a CNN and extract the features and feed them to LSTM for training. I am using 2 Layer CNN for feature extraction and im taking the features form fully connected layer and trying to feed them to LSTM. Problem is when i want to feed the FC layer to LSTM as input i get error regarding to wrong dimension. my FC layer is a Tensor with (128,1024) dimension. I tried to reshape it like this tf.reshape(fc,[-1]) which gives me a tensor ok (131072, )
dimension and still wont work. Could anyone give me any ideas of how im suppose to feed the FC to LSTM?here i just write part of my code and teh error i get.
Convolution Layer with 32 filters and a kernel size of 5
conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)
# Max Pooling (down-sampling) with strides of 2 and kernel size of 2
conv1 = tf.layers.max_pooling2d(conv1, 2, 2)
# Convolution Layer with 32 filters and a kernel size of 5
conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)
# Max Pooling (down-sampling) with strides of 2 and kernel size of 2
conv2 = tf.layers.max_pooling2d(conv2, 2, 2)
# Flatten the data to a 1-D vector for the fully connected layer
fc1 = tf.contrib.layers.flatten(conv2)
# Fully connected layer (in contrib folder for now)
fc1 = tf.layers.dense(fc1, 1024)
# Apply Dropout (if is_training is False, dropout is not applied)
fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)
s = tf.reshape(fc1, [1])
rnn_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
outputs, states = rnn.static_rnn(rnn_cell, s, dtype=tf.float32)
return tf.matmul(outputs[-1], rnn_weights['out']) + rnn_biases['out']
here is the error:
ValueError: Cannot reshape a tensor with 131072 elements to shape [1] (1 elements) for 'ConvNet/Reshape' (op: 'Reshape') with input shapes: [128,1024], [1] and with input tensors computed as partial shapes: input[1] = [1].
You have a logical error in how you approach the problem. Collapsing the data to a 1D tensor is not going to solve anything (even if you get it to work correctly).
If you are taking a sequence of images as input your input tensor should be 5D (batch, sequence_index, x, y, channel) or something permutation like that. conv2d should complain about the extra dimension but you probably missing one of them. You should try to fix it first.
Next use conv3d and max_pool3d with a window of 1 for the depth (since you don't want the different frames to interact at this stage).
When you are done you should still have 5D tensor, but x and y dimensions should be 1 (you should check this, and fix the operation if that's not the case).
The RNN part expects 3D tensors (batch, sequence_index, fature_index). You can use tf.squeeze to remove the 1 sized dimensions from your 5D tensor and get this 3D tensor. You shouldn't have to reshape anything.
If you don't use batches, it's OK, but the operations will still expect the dimension to be there (but for you it will be 1). Missing the dimension will cause problems with shapes down the line.