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import numpy as np
from keras.utils.data_utils import get_file
from config import MODEL_HDF5_PATH
LABELS = np.loadtxt("labels.txt", dtype=object, delimiter="\n")
def initialize_model():
# import the necessary packages
from keras.models import Sequential
from keras.layers import BatchNormalization
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.core import Flatten, Dropout, Dense
# CONV => RELU => POOL
cnn = Sequential()
inputShape = (224, 224, 3)
chanDim = -1
classes = 101
# Sequence of Convolution (scan filters), BatchNormalization (normalize numbers),
# MaxPooling (shrink tensor down), Dropout (prevent overfit)
cnn.add(
Conv2D(32, (3, 3), padding="same", input_shape=inputShape, activation="relu")
)
cnn.add(BatchNormalization(axis=chanDim))
cnn.add(MaxPooling2D(pool_size=(3, 3)))
cnn.add(Dropout(rate=0.25))
cnn.add(Conv2D(64, (3, 3), padding="same", activation="relu"))
cnn.add(BatchNormalization(axis=chanDim))
cnn.add(Conv2D(64, (3, 3), padding="same", activation="relu"))
cnn.add(BatchNormalization(axis=chanDim))
cnn.add(MaxPooling2D(pool_size=(2, 2)))
cnn.add(Dropout(rate=0.25))
cnn.add(Conv2D(128, (3, 3), padding="same", activation="relu"))
cnn.add(BatchNormalization(axis=chanDim))
cnn.add(Conv2D(128, (3, 3), padding="same", activation="relu"))
cnn.add(BatchNormalization(axis=chanDim))
cnn.add(MaxPooling2D(pool_size=(2, 2)))
cnn.add(Dropout(rate=0.25))
cnn.add(Flatten())
cnn.add(Dense(1024, activation="relu"))
cnn.add(BatchNormalization())
cnn.add(Dropout(rate=0.5))
# softmax classifier
cnn.add(Dense(classes, activation="softmax"))
return cnn
CNN = initialize_model()
CNN.load_weights(
get_file(
"weights.hdf5",
MODEL_HDF5_PATH,
cache_dir="."
)
)
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