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temporal_train_model.py
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temporal_train_model.py
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import numpy as np
from keras.models import Sequential, load_model
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.optimizers import SGD, Adam
from keras.layers.normalization import BatchNormalization
class ResearchModels():
def __init__(self, nb_classes, num_of_snip, opt_flow_len, image_shape = (224, 224), saved_model=None):
"""
`nb_classes` = the number of classes to predict
`opt_flow_len` = the length of optical flow frames
`image_shape` = shape of image frame
`saved_model` = the path to a saved Keras model to load
"""
self.num_of_snip = num_of_snip
self.opt_flow_len = opt_flow_len
self.load_model = load_model
self.saved_model = saved_model
self.nb_classes = nb_classes
print("Number of classes:")
print(self.nb_classes)
# Set the metrics. Only use top k if there's a need.
metrics = ['accuracy']
if self.nb_classes >= 10:
metrics.append('top_k_categorical_accuracy')
if self.saved_model is not None:
print("Loading model %s" % self.saved_model)
self.model = load_model(self.saved_model)
else:
print("Loading CNN model for the temporal stream.")
self.input_shape = (image_shape[0], image_shape[1], opt_flow_len * 2 * self.num_of_snip)
self.model = self.cnn_temporal()
optimizer = SGD(lr=1e-2, momentum=0.9, nesterov=True)
self.model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=metrics)
print(self.model.summary())
# CNN model for the temporal stream
def cnn_temporal(self):
print("Input shape:")
print(self.input_shape)
print("Numer of classes:")
print(self.nb_classes)
#model
model = Sequential()
#conv1
model.add(Conv2D(96, (7, 7), strides=2, padding='same', input_shape=self.input_shape))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
#conv2
model.add(Conv2D(256, (5, 5), strides=2, padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
#conv3
model.add(Conv2D(512, (3, 3), strides=1, activation='relu', padding='same'))
#conv4
model.add(Conv2D(512, (3, 3), strides=1, activation='relu', padding='same'))
#conv5
model.add(Conv2D(512, (3, 3), strides=1, activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
#full6
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.9))
#full7
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.9))
#softmax
model.add(Dense(self.nb_classes, activation='softmax'))
return model