I am trying to use the Dataset API along with Keras and I am trying to use the third option in the action plan mentioned here. Also I assumed that the third option was already done when seeing the second comment by #fchollet here.
But then when I tried to implement it, I got the following error:
When feeding symbolic tensors to a model, we expect the tensors to have
a static batch size. Got tensor with shape: (None, 32, 64, 64, 3)
I used the following strategy to fit the model:
training_filenames = [.....]
dataset = tf.data.TFRecordDataset(training_filenames)
dataset = dataset.map(_parse_function_all) # Parse the record into tensors.
dataset = dataset.batch(20)
iterator = dataset.make_initializable_iterator()
videos, labels= iterator.get_next()
model = create_base_network(input_shape = ( 32, 64, 64 3))
# output dimension will be (None, 10) for the model above
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
model.fit(videos, labels, , epochs=10, steps_per_epoch=1000)
I can solve the problem by using fit_generator. I found the solution here I applied #Dat-Nguyen's solution. But then I wasn't able to access the validation dataset within the custom callback in order to compute the AUC metric for example. So I need to use fit instead of fit_generator, but first need to git rid of this error.
So can anyone tell me why I got this error? Is the third step of fitting the model working now in Keras or does it still have issues?
so I figured out how to use keras with tf.DatasetAPI but without validation data. You can check out my question here Keras model.fit() with tf.dataset API iterator initializers
I think I discovered the problem. I am using standalone Keras, not the one imported from Tensorflow. The new feature of feeding the iterator directly to model.fit() is valid only when you are using tf.Keras, not standalone Keras.
Related
I have used this code from the TensorFlow website:
https://www.tensorflow.org/tutorials/images/transfer_learning_with_hub
I have to implement the entire code without any problem, but I am wondering how can I Fine-Tune it since I cannot access the:
base_model.trainable or base_model.layers
You could try setting trainable=True in
feature_extractor_layer = hub.KerasLayer(
feature_extractor_model,
input_shape=(224, 224, 3),
trainable=False)
Though it is better to keep the base layers frozen at least during the initial rounds of training.
I am trying to add data augmentation as a layer to a model but I am getting the following error.
TypeError: The added layer must be an instance of class Layer. Found: <tensorflow.python.keras.preprocessing.image.ImageDataGenerator object at 0x7f8c2dea0710>
data_augmentation = tf.keras.preprocessing.image.ImageDataGenerator(
rotation_range=30, horizontal_flip=True)
model = Sequential()
model.add(data_augmentation)
model.add(Dense(1028,input_shape=(final_features.shape[1],)))
model.add(Dropout(0.7,input_shape=(final_features.shape[1],)))
model.add(Dense(n_classes, activation= 'softmax', kernel_regularizer='l2'))
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(final_features, y,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2,
callbacks=[lrr,EarlyStop])
I have also tried this way:
data_augmentation = Sequential(
[
preprocessing.RandomFlip("horizontal"),
preprocessing.RandomRotation(0.1),
preprocessing.RandomZoom(0.1),
]
)
model = Sequential()
model.add(data_augmentation)
model.add(Dense(1028,input_shape=(final_features.shape[1],)))
model.add(Dropout(0.7,input_shape=(final_features.shape[1],)))
model.add(Dense(n_classes, activation= 'softmax', kernel_regularizer='l2'))
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(final_features, y,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2,
callbacks=[lrr,EarlyStop])
It gives an error:
ValueError: Input 0 of layer sequential_7 is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: [128, 14272]
Could you please advice how I can use augmentation in Keras?
In your first case, you are using ImageDataGenerator as a layer, which is not: as the name says, it is just a generator which applies random transformations to images (image augmentation) before feeding the network. So, the images are augmented in CPU and then feed to the neural network which can run in GPU if you have one.
Generators are usually used also to avoid loading huge datasets into memory since they allow to load only the batches being used soon.
In the second case, you are using image augmentation as layers of your model properly. The difference here is that the augmentation is run as part of your model, so if you have a GPU available for instance, those operations will run in GPU.
The problem with your second case is in the model itself (in fact the model is also wrong in the first approach, you only get an error there with the bad usage of ImageDataGenerator before your execution arrives to the model).
Note that you are using images as inputs, so, the input should be of shape (height, width, channels), but then you are starting your model with a dense layer, which expects a single array of shape (n_features,).
If your model needs to start with a Dense layer (strange, but may be ok in some case) then you need first to use Flatten layer to convert images of shape (h,w,c) into vectors of shape (h*w*c,). This change will solve your second approach for sure.
That said, you don't need to specify the input shape on every single layer: doing it in your first layer should be enough.
Last, but not least: are you sure this model is being feed with images? According to your fit call, it looks like you are using previously extracted features that may be vectors (this make sense with your current model architecture but makes no sense with the usage of image augmentation).
Please, provide more details with respect to your data to clarify this point.
I am working on a CNN that deals with super-resolution. It is required that I extract patches from the image, then train on these small patches (ie.41x41).
However, when it comes to predicting the image, the image is of a larger size than the patches. But Keras doesn't allow me to predict an image of larger size than the training images.
I have read Can Keras deal with input images with different size?. I have tried the way by putting None in my network input shape and then loading the weights. However, when it comes to this line: c1 = PReLU()(c1), I get the error: nt() argument must be a string, a bytes-like object or a number, not 'NoneType'. The code is attched below.
How can I fix this problem? I am using Keras with tensorflow backend. I have no fully connected layers, all are Conv2D with relu, except for the snippet below, is PReLU for c1.
Thanks.
input_shape = (None,None,1)
x = Input(shape = input_shape)
c1 = Convolution2D(64, (3,3), init = 'he_normal', padding='same', name='Conv1')(x)
c1 = PReLU()(c1)
#............................
output_img = keras.layers.add([x, finalconv])
model = Model(x, output_img)
Keras doesn't allow me to predict an image of larger size than the
training images
This is wrong, and keras allows you to do so when your network is designed properly.
However, when it comes to this line: c1 = PReLU()(c1), I get the
error: nt() argument must be a string, a bytes-like object or a
number, not 'NoneType'.
This error is expected because your input shape contains None. Actually, if you previously set shared_axes=[1,2] for PReLU (default value shared_axes=None), you will not see this error.
Therefore, the real issue here is that PReLU's parameters, previously set only for an 41x41 input, but now are asked to work for an arbitrary input size.
The best solution is to train a new model with input shape = (None,None,3) directly.
If you don't care about the possible degradation, you can load all layer weights of your pretrained model except for the PReLU layer. Then manually compute appropriate PReLU parameters can be shared across shared_axes =[1,2], and use it as the new PReLU parameters.
I am trying to learn to use the Keras Model API for modifying a trained model for the purpose of fine-tuning it on the go:
A very basic model:
inputs = Input((x_train.shape[1:]))
x = BatchNormalization(axis=1)(inputs)
x = Flatten()(x)
outputs = Dense(10, activation='softmax')(x)
model1 = Model(inputs, outputs)
model1.compile(optimizer=Adam(lr=1e-5), loss='categorical_crossentropy', metrics=['categorical_accuracy'])
The architecture of it is
InputLayer -> BatchNormalization -> Flatten -> Dense
After I do some training batches on it I want to add some extra Dense layer between the Flatten one and the outputs:
x = Dense(32,activation='relu')(model1.layers[-2].output)
outputs = model1.layers[-1](x)
However, when I run it, i get this:
ValueError: Input 0 is incompatible with layer dense_1: expected axis -1 of input shape to have value 784 but got shape (None, 32)
Could someone please explain what is going on and how/if can I add layers to an already trained model?
Thank you
A Dense layer is made strictly for a certain input dimension. That dimension cannot be changed after you define it (it would need a different number of weights).
So, if you really want to add layers before a dense layer that is already used, you need to make sure that the outputs of the last new layer is the same shape as the flatten's output. (It says you need 784, so your new last dense layer needs 784 units).
Another approach
Since you're adding intermediate layers, it's pointless to keep the last layer: it was trained specifically for a certain input, if you change the input, then you need to train it again.
Well... since you need to train it again anyway, why keep it? Just create a new one that will be suited to the shapes of your new previous layers.
My problem is to take all hidden outputs from an LSTM and use them as training examples for a single dense layer. Flattening the output of the hidden layers and feeding them to a dense layer is not what I am looking to do. I have tried the following things:
I have considered Timedistributed wrapper for the dense layer (https://keras.io/layers/wrappers/). But, this seems to apply the same layer to every time slice, which is not what I want. In other words, the Timedistributed wrapper has input_shape of a 3D tensor (number of samples, number of timesteps, number of features) and produces another 3D tensor of the same type: (number of samples, number of timesteps, number of features). Instead what I want is a 2D tensor as output, which looks like (number of samples*number of timesteps, number of features)
There was a pull request for an AdvancedReshapeLayer: https://github.com/fchollet/keras/pull/36 on GitHub. This seems to be exactly what I am looking for. Unfortunately, it appears like that pull request was closed with no conclusive outcome.
I tried to build my own lambda layer to accomplish what I want as follows:
A). model.add(LSTM(NUM_LSTM_UNITS, return_sequences=True, activation='tanh')) #
B). model.add(Lambda(lambda x: x, output_shape=lambda x: (x[0]*x[1], x[2])))
C). model.add(Dense(NUM_CLASSES, input_dim=NUM_LSTM_UNITS))
mode.output_shape after (A) prints: (BATCH_SIZE, NUM_TIME_STEPS, NUM_LSTM_UNITS) and model.output_shape after (B) prints: (BATCH_SIZE*NUM_OF_TIMESTEPS, NUM_LSTM_UNITS)
Which is exactly what I am trying to achieve.
Unfortunately, when I try to run step (C). I get the following error:
Input 0 is incompatible with layer dense_1: expected ndim=2, found
ndim=3
This is baffling since when I print model.output_shape after (B), I do indeed see (BATCH_SIZE*NUM_OF_TIMESTEPS, NUM_LSTM_UNITS), which is of ndim=2.
Really appreciate any help with this.
EDIT: When I try to use the functional API instead of a sequential model, I still get the same error on step (C)
You can use backend reshape which includes batch_size dimension.
def backend_reshape(x):
return backend.reshape(x, (-1, NUM_LSTM_UNITS))
model.add(Lambda(backend_reshape, output_shape=(NUM_LSTM_UNITS,)))