Model.py 文件源码

python
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项目:CNN-people-detect 作者: JTKBowers 项目源码 文件源码
def build_graph(self, nn_im_w, nn_im_h, num_colour_channels=3, weights=None, biases=None):
        num_outputs = 1 #ofc
        self.nn_im_w = nn_im_w
        self.nn_im_h = nn_im_h

        if weights is None:
            weights = [None, None, None, None, None]
        if biases is None:
            biases = [None, None, None, None, None]

        with tf.device('/cpu:0'):
            # Placeholder variables for the input image and output images
            self.x = tf.placeholder(tf.float32, shape=[None, nn_im_w*nn_im_h*3])
            self.y_ = tf.placeholder(tf.float32, shape=[None, num_outputs])
            self.threshold = tf.placeholder(tf.float32)

            # Build the convolutional and pooling layers
            conv1_output_channels = 32
            conv2_output_channels = 16
            conv3_output_channels = 8

            conv_layer_1_input = tf.reshape(self.x, [-1, nn_im_h, nn_im_w, num_colour_channels]) #The resized input image
            self.build_conv_layer(conv_layer_1_input, num_colour_channels, conv1_output_channels, initial_weights=weights[0], initial_biases=biases[0]) # layer 1
            self.build_conv_layer(self.layers[0][0], conv1_output_channels, conv2_output_channels, initial_weights=weights[1], initial_biases=biases[1])# layer 2
            self.build_conv_layer(self.layers[1][0], conv2_output_channels, conv3_output_channels, initial_weights=weights[2], initial_biases=biases[2])# layer 3

            # Build the fully connected layer
            convnet_output_w = nn_im_w//8
            convnet_output_h = nn_im_h//8

            fully_connected_layer_input = tf.reshape(self.layers[2][0], [-1, convnet_output_w * convnet_output_h * conv3_output_channels])
            self.build_fully_connected_layer(fully_connected_layer_input, convnet_output_w, convnet_output_h, conv3_output_channels, initial_weights=weights[3], initial_biases=biases[3])

            # The dropout stage and readout layer
            self.keep_prob, self.h_drop = self.dropout(self.layers[3][0])
            self.y_conv,_,_ = self.build_readout_layer(self.h_drop, num_outputs, initial_weights=weights[4], initial_biases=biases[4])

            self.mean_error =  tf.sqrt(tf.reduce_mean(tf.square(self.y_ - self.y_conv)))
            self.train_step = tf.train.AdamOptimizer(1e-4).minimize(self.mean_error)

            self.accuracy = (1.0 - tf.reduce_mean(tf.abs(self.y_ - tf.round(self.y_conv))))


            positive_examples = tf.greater_equal(self.y_, 0.5)
            negative_examples = tf.logical_not(positive_examples)
            positive_classifications = tf.greater_equal(self.y_conv, self.threshold)
            negative_classifications = tf.logical_not(positive_classifications)

            self.true_positive = tf.reduce_sum(tf.cast(tf.logical_and(positive_examples, positive_classifications),tf.int32)) # count the examples that are positive and classified as positive
            self.false_positive = tf.reduce_sum(tf.cast(tf.logical_and(negative_examples, positive_classifications),tf.int32)) # count the examples that are negative but classified as positive

            self.true_negative = tf.reduce_sum(tf.cast(tf.logical_and(negative_examples, negative_classifications),tf.int32)) # count the examples that are negative and classified as negative
            self.false_negative = tf.reduce_sum(tf.cast(tf.logical_and(positive_examples, negative_classifications),tf.int32)) # count the examples that are positive but classified as negative

            self.positive_count = tf.reduce_sum(tf.cast(positive_examples, tf.int32)) # count the examples that are positive
            self.negative_count = tf.reduce_sum(tf.cast(negative_examples, tf.int32)) # count the examples that are negative

            self.confusion_matrix = tf.reshape(tf.pack([self.true_positive, self.false_positive, self.false_negative, self.true_negative]), [2,2])

        self.sess.run(tf.initialize_all_variables())
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