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Update README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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David Rotermund
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@ -377,8 +377,277 @@ for epoch_id in range(0, epoch_max):
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## Test the example network performance
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|[tf.keras.models.load_model](https://www.tensorflow.org/api_docs/python/tf/keras/saving/load_model) | "Loads a model saved via model.save()."|
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|[network.evaluate()](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#evaluate) | "Returns the loss value & metrics values for the model in test mode."|
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```python
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from tensorflow import keras
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from DataGenerator import DataGenerator
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number_of_classes: int = 10
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size_of_batch_test: int = 100
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model_id: int = 49
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test_data = DataGenerator(
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train=False,
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size_of_batch=size_of_batch_test,
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number_of_classes=number_of_classes,
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do_shuffle=False,
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)
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keras.backend.clear_session()
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network = keras.models.load_model("./Model_" + str(model_id) + ".h5")
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test_loss, test_acc = network.evaluate(x=test_data)
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print(f"Correct: {test_acc * 100.0:.2f}%")
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```
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## How to extract the activities from the network
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```python
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from tensorflow import keras
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from DataGenerator import DataGenerator
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import numpy as np
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number_of_classes: int = 10
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size_of_batch_test: int = 100
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model_id: int = 49
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pattern_batch_id: int = 0
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pattern_id: int = 42
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test_data = DataGenerator(
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train=False,
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size_of_batch=size_of_batch_test,
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number_of_classes=number_of_classes,
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do_shuffle=False,
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)
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keras.backend.clear_session()
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network = keras.models.load_model("./Model_" + str(model_id) + ".h5")
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image, target = test_data.__getitem__(pattern_batch_id)
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the_target = target[pattern_id]
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print("Layer 1 (Conv1)")
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input_0 = image[pattern_id : pattern_id + 1, :, :, :]
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output_0 = network.layers[0](input_0)
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print("Input Shape:")
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print(input_0.shape)
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print("Output Shape:")
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print(output_0.numpy().shape)
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print("")
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print("Layer 2 (Pool1)")
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input_1 = output_0
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output_1 = network.layers[1](input_1)
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print("Input Shape:")
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print(input_1.numpy().shape)
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print("Output Shape:")
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print(output_1.numpy().shape)
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print("")
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print("Layer 3 (Conv2)")
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input_2 = output_1
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output_2 = network.layers[2](input_2)
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print("Input Shape:")
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print(input_2.numpy().shape)
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print("Output Shape:")
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print(output_2.numpy().shape)
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print("")
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print("Layer 4 (Pool2)")
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input_3 = output_2
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output_3 = network.layers[3](input_3)
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print("Input Shape:")
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print(input_3.numpy().shape)
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print("Output Shape:")
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print(output_3.numpy().shape)
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print("")
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print("Layer 5 (Flatten)")
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input_4 = output_3
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output_4 = network.layers[4](input_4)
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print("Input Shape:")
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print(input_4.numpy().shape)
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print("Output Shape:")
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print(output_4.numpy().shape)
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print("")
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print("Layer 6 (Full)")
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input_5 = output_4
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output_5 = network.layers[5](input_5)
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print("Input Shape:")
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print(input_5.numpy().shape)
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print("Output Shape:")
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print(output_5.numpy().shape)
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print("")
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print("Layer 7 (Output)")
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input_6 = output_5
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output_6 = network.layers[6](input_6)
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print("Input Shape:")
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print(input_6.numpy().shape)
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print("Output Shape:")
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print(output_6.numpy().shape)
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print("")
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print("\nEstimation")
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print(np.round(output_6.numpy(), 4))
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print("Strongest reponse is at " + str(np.argmax(output_6.numpy())))
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print("Correct output is " + str(np.argmax(the_target)))
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```
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## Extracting weight and bias
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Here is one way to extract the weights and bias of the whole network. Alternatively you can use [get_weights](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer#get_weights) from [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) in combination with [get_layer](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#get_layer) of [tf.keras.Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential).
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```python
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from tensorflow import keras
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from DataGenerator import DataGenerator
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number_of_classes: int = 10
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size_of_batch_test: int = 100
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model_id: int = 49
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pattern_batch_id: int = 0
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pattern_id: int = 42
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test_data = DataGenerator(
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train=False,
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size_of_batch=size_of_batch_test,
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number_of_classes=number_of_classes,
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do_shuffle=False,
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)
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keras.backend.clear_session()
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network = keras.models.load_model("./Model_" + str(model_id) + ".h5")
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weights_bias = network.get_weights()
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counter_layer: int = 0
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for i in range(0, len(weights_bias), 2):
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print("Layer " + str(counter_layer) + " weights_bias position: " + str(i) + " =>")
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print(weights_bias[i].shape)
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counter_layer += 1
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print("")
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counter_layer = 0
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for i in range(1, len(weights_bias), 2):
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print("Bias " + str(counter_layer) + " weights_bias position: " + str(i) + " =>")
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print(weights_bias[i].shape)
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counter_layer += 1
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```
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## Type of layers
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Reduced list with the most relevant network layers
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|[Activation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Activation)| Applies an activation function to an output.|
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|[AveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling1D)| Average pooling for temporal data.|
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|[AveragePooling2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D)| Average pooling operation for spatial data.|
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|[AveragePooling3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling3D) |Average pooling operation for 3D data (spatial or spatio-temporal).|
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|[BatchNormalization](https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization)| Layer that normalizes its inputs.|
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|[Conv1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D)| 1D convolution layer (e.g. temporal convolution).|
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|[Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D) |2D convolution layer (e.g. spatial convolution over images).|
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|[Conv3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv3D)| 3D convolution layer (e.g. spatial convolution over volumes).|
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|[Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense)| Just your regular densely-connected NN layer.|
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|[Dropout](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout) |Applies Dropout to the input.|
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|[Flatten](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten) |Flattens the input. Does not affect the batch size.|
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|[MaxPooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPooling1D)| Max pooling operation for 1D temporal data.|
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|[MaxPooling2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPooling2D) |Max pooling operation for 2D spatial data.|
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|[MaxPooling3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPooling3D) |Max pooling operation for 3D data (spatial or spatio-temporal).|
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|[SpatialDropout1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout1D) |Spatial 1D version of Dropout.|
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|[SpatialDropout2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout2D) |Spatial 2D version of Dropout.|
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|[SpatialDropout3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout3D) |Spatial 3D version of Dropout.|
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|[ZeroPadding1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding1D) |Zero-padding layer for 1D input (e.g. temporal sequence).|
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|[ZeroPadding2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding2D) |Zero-padding layer for 2D input (e.g. picture).|
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|[ZeroPadding3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding3D) |Zero-padding layer for 3D data (spatial or spatio-temporal).|
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Preprocessing layers
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Reduced list with the most relevant preprocessing layers
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CenterCrop A preprocessing layer which crops images.
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RandomContrast A preprocessing layer which randomly adjusts contrast during training.
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RandomCrop A preprocessing layer which randomly crops images during training.
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RandomFlip A preprocessing layer which randomly flips images during training.
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RandomHeight A preprocessing layer which randomly varies image height during training.
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RandomRotation A preprocessing layer which randomly rotates images during training.
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RandomTranslation A preprocessing layer which randomly translates images during training.
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RandomWidth A preprocessing layer which randomly varies image width during training.
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RandomZoom A preprocessing layer which randomly zooms images during training.
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Rescaling A preprocessing layer which rescales input values to a new range.
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Resizing A preprocessing layer which resizes images.
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Activation functions
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Reduced list with the most relevant activation functions
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hard_sigmoid(...) Hard sigmoid activation function.
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relu(...) Applies the rectified linear unit activation function.
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sigmoid(...) Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x)).
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softmax(...) Softmax converts a vector of values to a probability distribution.
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softplus(...) Softplus activation function, softplus(x) = log(exp(x) + 1).
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softsign(...) Softsign activation function, softsign(x) = x / (abs(x) + 1).
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tanh(...) Hyperbolic tangent activation function.
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Loss-functions
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Reduced list with the most relevant loss functions
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BinaryCrossentropy Computes the cross-entropy loss between true labels and predicted labels.
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CategoricalCrossentropy Computes the crossentropy loss between the labels and predictions.
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KLDivergence Computes Kullback-Leibler divergence loss between y_true and y_pred.
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MeanAbsoluteError Computes the mean of absolute difference between labels and predictions.
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MeanSquaredError Computes the mean of squares of errors between labels and predictions.
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Poisson Computes the Poisson loss between y_true and y_pred.
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SparseCategoricalCrossentropy Computes the crossentropy loss between the labels and predictions.
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Optimizer
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Reduced list with the most relevant optimizer
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Adagrad Optimizer that implements the Adagrad algorithm.
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Adam Optimizer that implements the Adam algorithm.
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RMSprop Optimizer that implements the RMSprop algorithm.
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SGD Gradient descent (with momentum) optimizer.
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Metrics
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A very reduced list with the most relevant metrics
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Accuracy Calculates how often predictions equal labels.
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BinaryAccuracy Calculates how often predictions match binary labels.
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BinaryCrossentropy Computes the crossentropy metric between the labels and predictions.
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CategoricalAccuracy Calculates how often predictions match one-hot labels.
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CategoricalCrossentropy Computes the crossentropy metric between the labels and predictions.
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KLDivergence Computes Kullback-Leibler divergence metric between y_true and y_pred.
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Mean Computes the (weighted) mean of the given values.
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MeanAbsoluteError Computes the mean absolute error between the labels and predictions.
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MeanSquaredError Computes the mean squared error between y_true and y_pred.
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Poisson Computes the Poisson metric between y_true and y_pred.
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Precision Computes the precision of the predictions with respect to the labels.
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RootMeanSquaredError Computes root mean squared error metric between y_true and y_pred.
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SparseCategoricalAccuracy Calculates how often predictions match integer labels.
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SparseCategoricalCrossentropy Computes the crossentropy metric between the labels and predictions.
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SparseTopKCategoricalAccuracy Computes how often integer targets are in the top K predictions.
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Sum Computes the (weighted) sum of the given values.
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TopKCategoricalAccuracy Computes how often targets are in the top K predictions.
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```python
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```
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