Get the convolutional layers of the model.
()
| 67 | |
| 68 | |
| 69 | def convolutional_layers(): |
| 70 | """ |
| 71 | Get the convolutional layers of the model. |
| 72 | |
| 73 | """ |
| 74 | x = tf.placeholder(tf.float32, [None, None, None]) |
| 75 | |
| 76 | # First layer |
| 77 | W_conv1 = weight_variable([5, 5, 1, 48]) |
| 78 | b_conv1 = bias_variable([48]) |
| 79 | x_expanded = tf.expand_dims(x, 3) |
| 80 | h_conv1 = tf.nn.relu(conv2d(x_expanded, W_conv1) + b_conv1) |
| 81 | h_pool1 = max_pool(h_conv1, ksize=(2, 2), stride=(2, 2)) |
| 82 | |
| 83 | # Second layer |
| 84 | W_conv2 = weight_variable([5, 5, 48, 64]) |
| 85 | b_conv2 = bias_variable([64]) |
| 86 | |
| 87 | h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) |
| 88 | h_pool2 = max_pool(h_conv2, ksize=(2, 1), stride=(2, 1)) |
| 89 | |
| 90 | # Third layer |
| 91 | W_conv3 = weight_variable([5, 5, 64, 128]) |
| 92 | b_conv3 = bias_variable([128]) |
| 93 | |
| 94 | h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3) |
| 95 | h_pool3 = max_pool(h_conv3, ksize=(2, 2), stride=(2, 2)) |
| 96 | |
| 97 | return x, h_pool3, [W_conv1, b_conv1, |
| 98 | W_conv2, b_conv2, |
| 99 | W_conv3, b_conv3] |
| 100 | |
| 101 | |
| 102 | def get_training_model(): |
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