I'm trying to predict image classes in keras (binary classification). The model accuracy is fine, but it seems that ImageDataGenerator shuffles the input images, so I was not able to match the predicted class with the original images.
datagen = ImageDataGenerator(rescale=1./255)
generator = datagen.flow_from_directory(
pred_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode=None,
shuffle=False,
save_to_dir='images/aug'.format(feature))
print model.predict_generator(generator, nb_input)
For example, if I have a1.jpg, a2.jpg,..., a9.jpg under pred_data_dir, I expect to get an array like
[class for a1.jpg, class for a2.jpg, ... class for a9.jpg]
from model.predict_generator(), but actually I got something like
[class for a3.jpg, class for a8.jpg, ... class for a2.jpg]
How can I resolve the issue?
Look at the source code of flow_from_directory. In my case, I had to rename all images. They were named 1.jpg .. 1000.jpg, but to be in order, they had to be named 0001.jpg .. 1000.jpg. The sorting is important here.
flow_from_directory uses sorted(os.listdir(directory)), thus the sorting is not always intuitive.
The flow_from_directory() method returns a DirectoryIterator object with a filenames member that lists all the files. Since that member is used for subsequent batch generation and iteration, you should be able to use it to match your filenames to predictions.
For your example, generator.filenames should give you a parallel list like ['a3.jpg', 'a8.jpg', ..., 'a2.jpg'].
Related
I try to construct a TFX pipeline with a trainer component with a Keras model defined like this:
def run_fn(fn_args: components.FnArgs):
transform_output = TFTransformOutput(fn_args.transform_output)
train_dataset = input_fn(fn_args.train_files,
fn_args.data_accessor,
transform_output,
num_batch)
eval_dataset = input_fn(fn_args.eval_files,
fn_args.data_accessor,
transform_output,
num_batch)
history = model.fit(train_dataset,
epochs=num_epochs,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps)
This works. However, if I change fitting to the following, this doesn't work:
history = model.fit(train_dataset,
epochs=num_epochs,
batch_size=num_batch,
validation_split=0.1)
Now, I have two questions:
Why does fitting work only with steps_per_epochs only? I couldn't find any explicit statement supporting this but this is the only way. Somehow I conclude that it must be something TFX specific (TFX handles input data only in a generator-like way?).
Let's say my train_dataset contains 100 instances and steps_per_epoch=1000 (with epochs=1). Is that mean that my 100 input instances are feed 10x each in order to reach the defined 1000 step? Isn't that counter-productive from training perspective?
I'm quite new to TFX (TensorFlow Extended), and have been going through the sample tutorial on the TensorFlow portal to understand a bit more to apply it to my dataset.
In my scenario, instead of predicting a single label, the problem at hand requires me to predict 2 outputs (category 1, category 2).
I've done this using pure TensorFlow Keras Functional API and that works fine, but then am now looking to see if that can be fitted into the TFX pipeline.
Where i get the error, is at the Trainer stage of the pipeline, and where it throws the error is in the _input_fn, and i suspect it's because i'm not correctly splitting out the given data into (features, labels) tensor pair in the pipeline.
Scenario:
Each row of the input data comes in the form of
[Col1, Col2, Col3, ClassificationA, ClassificationB]
ClassificationA and ClassificationB are the categorical labels which i'm trying to predict using the Keras Functional Model
The output layer of the keras functional model looks like below, where there's 2 outputs that is joined to a single dense layer (Note: _xf appended to the end is just to illustrate that i've encoded the classes to int representations)
output_1 = tf.keras.layers.Dense(
TargetA_Class, activation='sigmoid',
name = 'ClassificationA_xf')(dense)
output_2 = tf.keras.layers.Dense(
TargetB_Class, activation='sigmoid',
name = 'ClassificationB_xf')(dense)
model = tf.keras.Model(inputs = inputs,
outputs = [output_1, output_2])
In the trainer module file, i've imported the required packages at the start of the module file >
import tensorflow_transform as tft
from tfx.components.tuner.component import TunerFnResult
import tensorflow as tf
from typing import List, Text
from tfx.components.trainer.executor import TrainerFnArgs
from tfx.components.trainer.fn_args_utils import DataAccessor, FnArgs
from tfx_bsl.tfxio import dataset_options
The current input_fn in the trainer module file looks like the below (by following the tutorial)
def _input_fn(file_pattern: List[Text],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
batch_size: int = 200) -> tf.data.Dataset:
"""Helper function that Generates features and label dataset for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
tf_transform_output: A TFTransformOutput.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains (features, indices) tuple where features is a
dictionary of Tensors, and indices is a single Tensor of label indices.
"""
return data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size,
#label_key=[_transformed_name(x) for x in _CATEGORICAL_LABEL_KEYS]),
label_key=_transformed_name(_CATEGORICAL_LABEL_KEYS[0]), _transformed_name(_CATEGORICAL_LABEL_KEYS[1])),
tf_transform_output.transformed_metadata.schema)
When i run the trainer component the error that comes up is:
label_key=_transformed_name(_CATEGORICAL_LABEL_KEYS[0]),transformed_name(_CATEGORICAL_LABEL_KEYS1)),
^ SyntaxError: positional argument follows keyword argument
I've also tried label_key=[_transformed_name(x) for x in _CATEGORICAL_LABEL_KEYS]) which also gives an error.
However, if i just pass in a single label key, label_key=transformed_name(_CATEGORICAL_LABEL_KEYS[0]) then it works fine.
FYI - _CATEGORICAL_LABEL_KEYS is nothing but a list which contains the names of the 2 outputs i'm trying to predict (ClassificationA, ClassificationB).
transformed_name is nothing but a function to return an updated name/key for the transformed data:
def transformed_name(key):
return key + '_xf'
Question:
From what i can see, the label_key argument for dataset_options.TensorFlowDatasetOptions can only accept a single string/name of label, which means it may not be able to output the dataset with multi labels.
Is there a way which i can modify the _input_fn so that i can get the dataset that's returned by _input_fn to work with returning the 2 output labels? So the tensor that's returned looks something like:
Feature_Tensor: {Col1_xf: Col1_transformedfeature_values, Col2_xf:
Col2_transformedfeature_values, Col3_xf:
Col3_transformedfeature_values}
Label_Tensor: {ClassificationA_xf: ClassA_encodedlabels,
ClassificationB_xf: ClassB_encodedlabels}
Would appreciate advice from the wider community of tfx!
Since the label key is optional, maybe instead of specifying it in the TensorflowDatasetOptions, instead you can use dataset.map afterwards and pass both labels after taking them from your dataset.
Haven't tested it but something like:
def _data_augmentation(feature_dict):
features = feature_dict[_transformed_name(x) for x in
_CATEGORICAL_FEATURE_KEYS]]
keys=[_transformed_name(x) for x in _CATEGORICAL_LABEL_KEYS]
return features, keys
def _input_fn(file_pattern: List[Text],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
batch_size: int = 200) -> tf.data.Dataset:
"""Helper function that Generates features and label dataset for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
tf_transform_output: A TFTransformOutput.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains (features, indices) tuple where features is a
dictionary of Tensors, and indices is a single Tensor of label indices.
"""
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size,
tf_transform_output.transformed_metadata.schema)
dataset = dataset.map(_data_augmentation)
return dataset
I'm trying to make a network using augmentation.
First I use ImageDataGenerator with validation_split=0.2.
train_generator = ImageDataGenerator(
rotation_range=90,
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
fill_mode="nearest",
validation_split=0.2
)
Then I tried to create a augmented training data end a not augmented validation data.
I have to use flow instead of flow_from_directory.
train_augm = train_generator.flow([data_train, ebv_train], z_train, batch_size=128,subset='training')
valid_augm = train_generator.flow([data_train, ebv_train], z_train, batch_size=1,subset='validation')
I get this error menssage.
ValueError: Training and validation subsets have different number of classes after the split. If your numpy arrays are sorted by the label, you might want to shuffle them.
What I'm doing wrong?
The model.fit code is something like this
training_history = model.fit(
train_augm,
steps_per_epoch= len(data_train)//128,
epochs=10,
validation_data=valid_augm
)
The number of classes in the training data is not equal to the number of classes in the validation data. If you didn't shuffle it, please shuffle it. If you're still getting the error, I am assuming that some of the class has a very small number of data. you can reshuffle it, but sometimes you will get the same error. What you can do is, add more data to that specific class or manually split into training and validation.
For random split, you can take a look at train_test_split library.
I would like to know if Keras can be used as an interface to TensoFlow for only doing computation on my GPU.
I tested TF directly on my GPU. But for ML purposes, I started using Keras, including the backend. I would find it 'comfortable' to do all my stuff in Keras instead of Using two tools.
This is also a matter of curiosity.
I found some examples like this one:
http://christopher5106.github.io/deep/learning/2018/10/28/understand-batch-matrix-multiplication.html
However this example does not actually do the calculation.
It also does not get input data.
I duplicate the snippet here:
'''
from keras import backend as K
a = K.ones((3,4))
b = K.ones((4,5))
c = K.dot(a, b)
print(c.shape)
'''
I would simply like to know if I can get the result numbers from this snippet above, and how?
Thanks,
Michel
Keras doesn't have an eager mode like Tensorflow, and it depends on models or functions with "placeholders" to receive and output data.
So, it's a little more complicated than Tensorflow to do basic calculations like this.
So, the most user friendly solution would be creating a dummy model with one Lambda layer. (And be careful with the first dimension that Keras will insist to understand as a batch dimension and require that input and output have the same batch size)
def your_function_here(inputs):
#if you have more than one tensor for the inputs, it's a list:
input1, input2, input3 = inputs
#if you don't have a batch, you should probably have a first dimension = 1 and get
input1 = input1[0]
#do your calculations here
#if you used the batch_size=1 workaround as above, add this dimension again:
output = K.expand_dims(output,0)
return output
Create your model:
inputs = Input(input_shape)
#maybe inputs2 ....
outputs = Lambda(your_function_here)(list_of_inputs)
#maybe outputs2
model = Model(inputs, outputs)
And use it to predict the result:
print(model.predict(input_data))
I am building a recommendation system where I predict the best item for each user given their purchase history of items. I have userIDs and itemIDs and how much itemID was purchased by userID. I have Millions of users and thousands of products. Not all products are purchased(there are some products that no one has bought them yet). Since the users and items are big I don't want to use one-hot vectors. I am using pytorch and I want to create and train the embeddings so that I can make the predictions for each user-item pair. I followed this tutorial https://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html. If it's an accurate assumption that the embedding layer is being trained, then do I retrieve the learned weights through model.parameters() method or should I use the embedding.data.weight option?
model.parameters() returns all the parameters of your model, including the embeddings.
So all these parameters of your model are handed over to the optimizer (line below) and will be trained later when calling optimizer.step() - so yes your embeddings are trained along with all other parameters of the network.(you can also freeze certain layers by setting i.e. embedding.weight.requires_grad = False, but this is not the case here).
# summing it up:
# this line specifies which parameters are trained with the optimizer
# model.parameters() just returns all parameters
# embedding class weights are also parameters and will thus be trained
optimizer = optim.SGD(model.parameters(), lr=0.001)
You can see that your embedding weights are also of type Parameter by doing so:
import torch
embedding_maxtrix = torch.nn.Embedding(10, 10)
print(type(embedding_maxtrix.weight))
This will output the type of the weights, which is Parameter:
<class 'torch.nn.parameter.Parameter'>
I'm not entirely sure what mean by retrieve. Do you mean getting a single vector, or do you want just the whole matrix to save it, or do something else?
embedding_maxtrix = torch.nn.Embedding(5, 5)
# this will get you a single embedding vector
print('Getting a single vector:\n', embedding_maxtrix(torch.LongTensor([0])))
# of course you can do the same for a seqeunce
print('Getting vectors for a sequence:\n', embedding_maxtrix(torch.LongTensor([1, 2, 3])))
# this will give the the whole embedding matrix
print('Getting weights:\n', embedding_maxtrix.weight.data)
Output:
Getting a single vector:
tensor([[-0.0144, -0.6245, 1.3611, -1.0753, 0.5020]], grad_fn=<EmbeddingBackward>)
Getting vectors for a sequence:
tensor([[ 0.9277, -0.1879, -1.4999, 0.2895, 0.8367],
[-0.1167, -2.2139, 1.6918, -0.3483, 0.3508],
[ 2.3763, -1.3408, -0.9531, 2.2081, -1.5502]],
grad_fn=<EmbeddingBackward>)
Getting weights:
tensor([[-0.0144, -0.6245, 1.3611, -1.0753, 0.5020],
[ 0.9277, -0.1879, -1.4999, 0.2895, 0.8367],
[-0.1167, -2.2139, 1.6918, -0.3483, 0.3508],
[ 2.3763, -1.3408, -0.9531, 2.2081, -1.5502],
[-0.5829, -0.1918, -0.8079, 0.6922, -0.2627]])
I hope this answers your question, you can also take a look at the documentation, there you can find some useful examples as well.
https://pytorch.org/docs/stable/nn.html#torch.nn.Embedding