How do you use Keras LeakyReLU in Python? - python

I am trying to produce a CNN using Keras, and wrote the following code:
batch_size = 64
epochs = 20
num_classes = 5
cnn_model = Sequential()
cnn_model.add(Conv2D(32, kernel_size=(3, 3), activation='linear',
input_shape=(380, 380, 1), padding='same'))
cnn_model.add(Activation('relu'))
cnn_model.add(MaxPooling2D((2, 2), padding='same'))
cnn_model.add(Conv2D(64, (3, 3), activation='linear', padding='same'))
cnn_model.add(Activation('relu'))
cnn_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
cnn_model.add(Conv2D(128, (3, 3), activation='linear', padding='same'))
cnn_model.add(Activation('relu'))
cnn_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
cnn_model.add(Flatten())
cnn_model.add(Dense(128, activation='linear'))
cnn_model.add(Activation('relu'))
cnn_model.add(Dense(num_classes, activation='softmax'))
cnn_model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(), metrics=['accuracy'])
I want to use Keras's LeakyReLU activation layer instead of using Activation('relu'). However, I tried using LeakyReLU(alpha=0.1) in place, but this is an activation layer in Keras, and I get an error about using an activation layer and not an activation function.
How can I use LeakyReLU in this example?

All advanced activations in Keras, including LeakyReLU, are available as layers, and not as activations; therefore, you should use it as such:
from keras.layers import LeakyReLU
# instead of cnn_model.add(Activation('relu'))
# use
cnn_model.add(LeakyReLU(alpha=0.1))

Sometimes you just want a drop-in replacement for a built-in activation layer, and not having to add extra activation layers just for this purpose.
For that, you can use the fact that the activation argument can be a callable object.
lrelu = lambda x: tf.keras.activations.relu(x, alpha=0.1)
model.add(Conv2D(..., activation=lrelu, ...)
Since a Layer is also a callable object, you could also simply use
model.add(Conv2D(..., activation=tf.keras.layers.LeakyReLU(alpha=0.1), ...)
which now works in TF2. This is a better solution as this avoids the need to use a custom_object during loading as #ChristophorusReyhan mentionned.

you can import the function to make the code cleaner and then use it like any other activation.
if you choose not to define alpha, don't forget to add brackets "LeakyReLU()"
from tensorflow.keras.layers import LeakyReLU
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(512, activation=LeakyReLU()))
model.add(tf.keras.layers.Dense(512, activation=LeakyReLU(alpha=0.1)))

Related

Configuration of CNN model for recognition of sequential data - Architecture of the top of the CNN - Parallel Layers

I am trying to configure a network for character recognition of sequential data like license plates.
Now I would like to use the architecture which is noted in Table 3 in Deep Automatic Licence Plate Recognition system (link: http://www.ee.iisc.ac.in/people/faculty/soma.biswas/Papers/jain_icgvip2016_alpr.pdf).
The architecture the authors presented is this one:
The first layers are very common, but where I was stumbling was the top (the part in the red frame) of the architecture. They mention 11 parallel layers and I am really unsure how to get this in Python. I coded this architecture but it does not seem to be right to me.
model = Sequential()
model.add(Conv2D(64, kernel_size=(5, 5), input_shape = (32, 96, 3), activation = "relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(3, 3), activation = "relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=(3, 3), activation = "relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024, activation = "relu"))
model.add(Dense(11*37, activation="Softmax"))
model.add(keras.layers.Reshape((11, 37)))
Could someone help? How do I have to code the top to get an equal architecture like the authors?
The code below can build the architecture described in the image.
import tensorflow as tf
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Input, Reshape, Concatenate, Dropout
def create_model(input_shape = (32, 96, 1)):
input_img = Input(shape=input_shape)
'''
Add the ST Layer here.
'''
model = Conv2D(64, kernel_size=(5, 5), input_shape = input_shape, activation = "relu")(input_img)
model = MaxPooling2D(pool_size=(2, 2))(model)
model = Dropout(0.25)(model)
model = Conv2D(128, kernel_size=(3, 3), input_shape = input_shape, activation = "relu")(model)
model = MaxPooling2D(pool_size=(2, 2))(model)
model = Dropout(0.25)(model)
model = Conv2D(256, kernel_size=(3, 3), input_shape = input_shape, activation = "relu")(model)
model = MaxPooling2D(pool_size=(2, 2))(model)
model = Dropout(0.25)(model)
model = Flatten()(model)
backbone = Dense(1024, activation="relu")(model)
branches = []
for i in range(11):
branches.append(backbone)
branches[i] = Dense(37, activation = "softmax", name="branch_"+str(i))(branches[i])
output = Concatenate(axis=1)(branches)
output = Reshape((11, 37))(output)
model = Model(input_img, output)
return model
From my understanding, your implementation is almost correct. The authors train 11 individual classifiers taking as input the output from the Fully Connected Layer. Here, you can think of "parallel" as "independent".
However, you cannot apply the Softmax activation right after the Fully Connected Layer. Since all the classifiers are independent, we want each of them to output a probability for each possible character. Putting things differently, we want the sum of the outputs of each classifier to be 1. Hence, the correct implementation would be:
...
model.add(Dense(1024, activation = "relu"))
# Feeding every neuron with the previous layer's output
model.add(Dense(11*37))
model.add(keras.layers.Reshape((11, 37)))
model.add(keras.activations.softmax(x, axis=1))

error when making my own activation function

I am trying to test an activation function on MNIST data
but it gave an error:
TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.
Here is my activation function :
def fun1(x):
return np.sqrt(x) if x>=0 else (-(np.sqrt(-x)))
and here is the model:
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), kernel_regularizer=regularizers.l2(w_l2),
input_shape=input_shape))
model.add(BatchNormalization())
model.add(Activation(fun1))
model.add(Conv2D(64, (3, 3), kernel_regularizer=regularizers.l2(w_l2)))
model.add(BatchNormalization())
model.add(Activation(fun1))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, kernel_regularizer=regularizers.l2(w_l2)))
model.add(BatchNormalization())
model.add(Activation(fun1))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=['accuracy'])
model.summary()
In the custom activation function, x is a tensor, so Keras backend methods need to be used instead of numpy. Your implementation could be changed to something like this:
from keras import backend as K
def fun1(x):
return K.sqrt(K.abs(x))
For more examples, look at Keras defined activations:
https://github.com/keras-team/keras/blob/master/keras/activations.py

Connecting recurrent layer after cnn, what does tf.expand_dims do?

I want to use a model for classifying eight class of images. I think using convolutional layers before recurrent layers can work for my problem. But, there is a problem using recurrent layers immediately after convolutional or dense layer which causes tensorflow give following error.
Input 0 is incompatible with layer simple_rnn_1: expected ndim=3, found ndim=2
I solve this problem using Tensorflow expand_dims() function using inside a Lambda layer. It seems worked properly but, I want to be sure about my model is working right. Despite looking at relevant documentation, I couldn't understand what expand_dims() done to make the model work.
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dropout, Flatten, Dense, Lambda, SimpleRNN
from tensorflow import expand_dims
def create_model():
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(50, 50, 1), padding='same',
activation='relu'))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Lambda(lambda x: expand_dims(model.output, axis=-1)))
model.add(SimpleRNN(64, return_sequences=False))
model.add(Dense(units=8, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
return model
I want to interpret CNN output as sequential information using recurrent layers (LSTM, GRU, other recurrent models). Am I doing right using Lambda layer with expand_dims()?
The Flatten layer reduces the dimensions of the images to (Batch Length, Feature dimensions), and the recurrent neural networks expect 3 dimensions for the input instead of 2, as it needs a new dimension for the time-series inputs as in (Batch Length, Time Dimensions, Feature Dimensions)
The expand_dims simply adds an extra dimension that transforms your flattened outputs to (Batch Length, Features Dimensions, 1). This means that the RNN would now work, and it will consider the features dimensions as the time dimension for the data, thus going throw them in an orderly fashion and doing what you intended.

Keras ConvLSTM2D: why use the averagepooling3d and how to to regression

i have been studying Keras ConvLSTM2D: ValueError on output layer
i want to use the same code but i want to do regression ( single value ).
I dont know how to do this. And i also dont understand the use of last layers of this post code. Why is averagepolling3d used?
the code from link is
model = Sequential()
model.add(ConvLSTM2D(
filters=40,
kernel_size=(3, 3),
input_shape=(None, 135, 240, 1),
padding='same',
return_sequences=True))
model.add(BatchNormalization())
model.add(ConvLSTM2D(
filters=40,
kernel_size=(3, 3),
padding='same',
return_sequences=True))
model.add(BatchNormalization())
model.add(ConvLSTM2D(
filters=40,
kernel_size=(3, 3),
padding='same',
return_sequences=True))
model.add(BatchNormalization())
model.add(AveragePooling3D((1, 135, 240)))
model.add(Reshape((-1, 40)))
model.add(Dense(
units=9,
activation='sigmoid'))
model.compile(
loss='categorical_crossentropy',
optimizer='adadelta'
)
AveragePooling3D is used to reduce each frame in a sequence to a single value + to reduce the #parameters in the Dense Layer. So, the dimension becomes (None, 40 , 1 , 1 ,1 ). Then, using Reshape allows it to use for fully-connected part.
Also, as in Keras ConvLSTM2D: ValueError on output layer, AveragePooling3D is used instead of GlobalMaxPooling2D since data is 5D and Global operations leaves only (batch_size, channels) which is not desirable in your case.

Extracting last layers of keras model as a submodel

Say we have a convolutional neural network M. I can extract features from images by using
extractor = Model(M.inputs, M.get_layer('last_conv').output)
features = extractor.predict(X)
How can I get the model that will predict classes using features?
I can't use the following lines because it requires the input of the model to be a placeholder.
predictor = Model([M.get_layer('next_layer').input], M.outputs)
pred = predictor.predict(features)
I also can't use K.function because later I want to use it as part of another model, so I will be appliyng predictor to tf.tensor, not np.array.
This is not the nicest solution, but it works:
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
def cnn():
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=(28, 28, 1), name='l_01'))
model.add(Conv2D(64, (3, 3), activation='relu', name='l_02'))
model.add(MaxPooling2D(pool_size=(2, 2), name='l_03'))
model.add(Dropout(0.25, name='l_04'))
model.add(Flatten(name='l_05'))
model.add(Dense(128, activation='relu', name='l_06'))
model.add(Dropout(0.5, name='l_07'))
model.add(Dense(10, activation='softmax', name='l_08'))
return model
def predictor(input_shape):
model = Sequential()
model.add(Flatten(name='l_05', input_shape=(12, 12, 64)))
model.add(Dense(128, activation='relu', name='l_06'))
model.add(Dropout(0.5, name='l_07'))
model.add(Dense(10, activation='softmax', name='l_08'))
return model
cnn_model = cnn()
cnn_model.save('/tmp/cnn_model.h5')
predictor_model = predictor(cnn_model.output.shape)
predictor_model.load_weights('/tmp/cnn_model.h5', by_name=True)
Every layer in the model is indexed. So if you know which layers you need, you could loop through them, copying them into a new model. This operation should copy the weights inside the layer as well.
Here's a model (from Oli Blum's answer):
model = Sequential()
# add some layers
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=(28, 28, 1), name='l_01'))
model.add(Conv2D(64, (3, 3), activation='relu', name='l_02'))
model.add(MaxPooling2D(pool_size=(2, 2), name='l_03'))
model.add(Dropout(0.25, name='l_04'))
model.add(Flatten(name='l_05'))
model.add(Dense(128, activation='relu', name='l_06'))
model.add(Dropout(0.5, name='l_07'))
model.add(Dense(10, activation='softmax', name='l_08'))
Say you wanted the last three layers:
def extract_layers(main_model, starting_layer_ix, ending_layer_ix):
# create an empty model
new_model = Sequential()
for ix in range(starting_layer_ix, ending_layer_ix + 1):
curr_layer = main_model.get_layer(index=ix)
# copy this layer over to the new model
new_model.add(curr_layer)
return new_model
It depends on what you want to do.
If you are going to throw away the feature extractor afterwards
If you plan on training the feature extractor later
If you are going to use the extracted features but you don't intend on training the model used to generate them, you could use the predict method to get the features as you did:
features = extractor.predict(X)
then save its output to a file (np.save or cPickle or whatever).
After that you could use that new dataset as the input to a new model.
If you plan on training the feature extractor later you'll need to stack the two networks as seen here with vgg as feature extractor https://github.com/fchollet/keras/issues/4576:
img_width, img_height = 150, 150
vgg16_model = VGG16(include_top=False, weights='imagenet')
input = Input(batch_shape=vgg16_model.output_shape)
x = GlobalAveragePooling2D()(input)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
predict = Dense(1, activation='sigmoid')(x)
top_model = Model(input, predict)
top_model.load_weights(os.path.join(data_path, 'VGG16Classifier.hdf5'))
input = Input(shape=(3, img_width, img_height))
x = vgg16_model(input)
predict = top_model(x)
model = Model(input, predict)
PS: This example uses channels first ordering. If you are using tensorflow you should change the shape to shape=(img_width, img_height,3 )

Categories

Resources