Datasets:
HenryAI
/
KerasBERTv1-Data

Dataset card Data Studio Files Community
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4.99k
columns_data,
figsize=(fig_width, fig_height),
gridspec_kw={\"height_ratios\": heights},
)
for i in range(rows_data):
for j in range(columns_data):
axarr[i, j].imshow(data[i][j], cmap=\"gray\")
axarr[i, j].axis(\"off\")
plt.subplots_adjust(wspace=0, hspace=0, left=0, right=1, bottom=0, top=1)
plt.show()
# Visualize montage of slices.
# 4 rows and 10 columns for 100 slices of the CT scan.
plot_slices(4, 10, 128, 128, image[:, :, :40])
png
Define a 3D convolutional neural network
To make the model easier to understand, we structure it into blocks. The architecture of the 3D CNN used in this example is based on this paper.
def get_model(width=128, height=128, depth=64):
\"\"\"Build a 3D convolutional neural network model.\"\"\"
inputs = keras.Input((width, height, depth, 1))
x = layers.Conv3D(filters=64, kernel_size=3, activation=\"relu\")(inputs)
x = layers.MaxPool3D(pool_size=2)(x)
x = layers.BatchNormalization()(x)
x = layers.Conv3D(filters=64, kernel_size=3, activation=\"relu\")(x)
x = layers.MaxPool3D(pool_size=2)(x)
x = layers.BatchNormalization()(x)
x = layers.Conv3D(filters=128, kernel_size=3, activation=\"relu\")(x)
x = layers.MaxPool3D(pool_size=2)(x)
x = layers.BatchNormalization()(x)
x = layers.Conv3D(filters=256, kernel_size=3, activation=\"relu\")(x)
x = layers.MaxPool3D(pool_size=2)(x)
x = layers.BatchNormalization()(x)
x = layers.GlobalAveragePooling3D()(x)
x = layers.Dense(units=512, activation=\"relu\")(x)
x = layers.Dropout(0.3)(x)
outputs = layers.Dense(units=1, activation=\"sigmoid\")(x)
# Define the model.
model = keras.Model(inputs, outputs, name=\"3dcnn\")
return model
# Build model.
model = get_model(width=128, height=128, depth=64)
model.summary()
Model: \"3dcnn\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 128, 128, 64, 1)] 0
_________________________________________________________________
conv3d (Conv3D) (None, 126, 126, 62, 64) 1792
_________________________________________________________________
max_pooling3d (MaxPooling3D) (None, 63, 63, 31, 64) 0
_________________________________________________________________
batch_normalization (BatchNo (None, 63, 63, 31, 64) 256
_________________________________________________________________
conv3d_1 (Conv3D) (None, 61, 61, 29, 64) 110656
_________________________________________________________________
max_pooling3d_1 (MaxPooling3 (None, 30, 30, 14, 64) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 30, 30, 14, 64) 256
_________________________________________________________________
conv3d_2 (Conv3D) (None, 28, 28, 12, 128) 221312
_________________________________________________________________
max_pooling3d_2 (MaxPooling3 (None, 14, 14, 6, 128) 0
_________________________________________________________________
batch_normalization_2 (Batch (None, 14, 14, 6, 128) 512
_________________________________________________________________
conv3d_3 (Conv3D) (None, 12, 12, 4, 256) 884992
_________________________________________________________________
max_pooling3d_3 (MaxPooling3 (None, 6, 6, 2, 256) 0
_________________________________________________________________
batch_normalization_3 (Batch (None, 6, 6, 2, 256) 1024
_________________________________________________________________
global_average_pooling3d (Gl (None, 256) 0
_________________________________________________________________
dense (Dense) (None, 512) 131584
_________________________________________________________________
dropout (Dropout) (None, 512) 0
_________________________________________________________________
dense_1 (Dense) (None, 1) 513
=================================================================
Total params: 1,352,897
Trainable params: 1,351,873
Non-trainable params: 1,024
_________________________________________________________________
Train model
# Compile model.
initial_learning_rate = 0.0001