from __future__ import absolute_import
from __future__ import print_function
import numpy as np
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Input, Flatten, Dense, Conv3D, MaxPool3D,MaxPooling2D, PReLU
def _3d_cnn_model(input_shape, num_classes):
# Define Model
inputs = Input(shape=input_shape, name="input-layer")
# Conv 1
X = Conv3D(filters=16, kernel_size=(3, 1, 5), strides=(1, 1, 1), name="conv1-1")(inputs)
X = PReLU(name="activation1-1")(X)
X = Conv3D(filters=16, kernel_size=(3, 9, 1), strides=(1, 2, 1), name="conv1-2")(X)
X = PReLU(name="activation1-2")(X)
X = MaxPool3D(pool_size=(1, 1, 2), strides=(1, 1, 2), padding="valid", name="pool-1")(X)
# X = Dropout(0.2)(X)
# Conv 2
X = Conv3D(filters=32, kernel_size=(3, 1, 4), strides=(1, 1, 1), name="conv2-1")(X)
X = PReLU(name="activation2-1")(X)
X = Conv3D(filters=32, kernel_size=(3, 8, 1), strides=(1, 2, 1), name="conv2-2")(X)
X = PReLU(name="activation2-2")(X)
X = MaxPool3D(pool_size=(1, 1, 2), strides=(1, 1, 2), padding="valid", name="pool-2")(X)
# X = Dropout(0.2)(X)
# Conv 3
X = Conv3D(filters=64, kernel_size=(3, 1, 3), strides=(1, 1, 1), name="conv3-1")(X)
X = PReLU(name="activation3-1")(X)
X = Conv3D(filters=64, kernel_size=(3, 7, 1), strides=(1, 1, 1), name="conv3-2")(X)
X = PReLU(name="activation3-2")(X)
# X = Dropout(0.2)(X)
# Conv 4
X = Conv3D(filters=128, kernel_size=(3, 1, 3), strides=(1, 1, 1), name="conv4-1")(X)
X = PReLU(name="activation4-1")(X)
X = Conv3D(filters=128, kernel_size=(3, 7, 1), strides=(1, 1, 1), name="conv4-2")(X)
X = PReLU(name="activation4-2")(X)
# X = Dropout(0.2)(X)
# Flaten
X = Flatten()(X)
# FC
X = Dense(units=128, name="fc", activation='relu')(X)
# Final Activation
X = Dense(units=num_classes, activation='softmax', name="ac_softmax")(X)
model = Model(inputs=inputs, outputs=X)
return model
