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initial pass at conv layer viz
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‎kviz/conv.py

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"""
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Copyright 2021 Lance Galletti
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import numpy as np
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from PIL import Image as im
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import matplotlib.pyplot as plt
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from tensorflow.keras import models
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class ConvGraph():
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"""
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Class for creating and rendering visualization of Keras
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Sequential Model with Convolutional Layers
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Attributes:
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model : tf.keras.Model
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a compiled keras sequential model
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Methods:
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render :
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Shows all the convolution activations
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"""
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def __init__(self, model):
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self.model = model
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def _snap_layer(self, display_grid, scale, filename, xticks, yticks):
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fig, ax = plt.subplots(figsize=(int(scale * display_grid.shape[1]), int(scale * display_grid.shape[0])))
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ax.set_xticks(xticks)
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ax.set_yticks(yticks)
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ax.grid(True)
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ax.imshow(display_grid, aspect='auto')
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fig.savefig(filename + '.png', transparent=True)
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plt.close()
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return np.asarray(im.open(filename + '.png'))
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def animate(self, X=None, filename='conv_animation'):
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"""
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Render animation of a Convolutional layers based on a stream
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of input.
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Parameters:
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X : ndarray
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input to a Keras model - ideally of the same class
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filename : str
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name of file to which visualization will be saved
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Returns:
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None
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"""
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layer_outputs = [layer.output for layer in self.model.layers]
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# Creates a model that will return these outputs, given the model input
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activation_model = models.Model(inputs=self.model.input, outputs=layer_outputs)
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images_per_row = 8
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for i in range(len(self.model.layers)):
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# Ignore non-conv2d layers
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layer_name = self.model.layers[i].name
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if not layer_name.startswith("conv2d"):
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continue
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images = []
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for j in range(len(X)):
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activations = activation_model.predict(X[j])
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# Number of features in the feature map
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n_features = activations[i].shape[-1]
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# The feature map has shape (1, size, size, n_features).
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size = activations[i].shape[1]
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# Tiles the activation channels in this matrix
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n_cols = n_features // images_per_row
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display_grid = np.zeros((size * n_cols, images_per_row * size))
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# Tiles each filter into a big horizontal grid
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for col in range(n_cols):
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for row in range(images_per_row):
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# Displays the grid
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display_grid[
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col * size: (col + 1) * size,
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row * size: (row + 1) * size] = activations[i][0, :, :, col * images_per_row + row]
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images.append(
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im.fromarray(self._snap_layer(
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display_grid, 1. / size,
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filename + "_" + layer_name,
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xticks=np.linspace(0, display_grid.shape[1], images_per_row + 1),
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yticks=np.linspace(0, display_grid.shape[0], n_cols + 1))))
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images[0].save(
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filename + "_" + layer_name + '.gif',
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optimize=False, # important for transparent background
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save_all=True,
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append_images=images[1:],
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loop=0,
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duration=100,
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transparency=255, # prevent PIL from making background black
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disposal=2
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)
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return
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def render(self, X=None, filename='conv_filters'):
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"""
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Render visualization of a Convolutional keras model
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Parameters:
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X : ndarray
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input to a Keras model
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filename : str
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name of file to which visualization will be saved
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Returns:
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None
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"""
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layer_outputs = [layer.output for layer in self.model.layers]
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# Creates a model that will return these outputs, given the model input
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activation_model = models.Model(inputs=self.model.input, outputs=layer_outputs)
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images_per_row = 8
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for j in range(len(X)):
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activations = activation_model.predict(X[j])
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for i in range(len(activations)):
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# Ignore non-conv2d layers
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layer_name = self.model.layers[i].name
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if not layer_name.startswith("conv2d"):
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continue
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# Number of features in the feature map
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n_features = activations[i].shape[-1]
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# The feature map has shape (1, size, size, n_features).
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size = activations[i].shape[1]
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# Tiles the activation channels in this matrix
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n_cols = n_features // images_per_row
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display_grid = np.zeros((size * n_cols, images_per_row * size))
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# Tiles each filter into a big horizontal grid
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for col in range(n_cols):
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for row in range(images_per_row):
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# Displays the grid
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display_grid[
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col * size: (col + 1) * size,
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row * size: (row + 1) * size] = activations[i][0, :, :, col * images_per_row + row]
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self._snap_layer(
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display_grid, 1. / size,
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filename + "_" + str(j) + "_" + layer_name,
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xticks=np.linspace(0, display_grid.shape[1], images_per_row + 1),
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yticks=np.linspace(0, display_grid.shape[0], n_cols + 1))
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return

‎kviz/dense.py

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Original file line numberDiff line numberDiff line change
@@ -49,6 +49,10 @@ class DenseGraph():
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is provided, show a GIF of the activations of each
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Neuron based on the input provided.
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animate_learning:
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Make GIF from snapshots of decision boundary at
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given snap_freq
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"""
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def __init__(self, model):

‎tests/test_conv.py

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import numpy as np
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from tensorflow import keras
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from tensorflow.keras import layers, utils
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from tensorflow.keras.datasets import mnist
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from kviz.conv import ConvGraph
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def test_conv_input():
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
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X_train = X_train.astype('float32')
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X_train /= 255
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X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
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X_test = X_test.astype('float32')
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X_test /= 255
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number_of_classes = 10
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Y_train = utils.to_categorical(y_train, number_of_classes)
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Y_test = utils.to_categorical(y_test, number_of_classes)
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ACTIVATION = "relu"
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model = keras.models.Sequential()
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model.add(layers.Conv2D(32, 5, input_shape=(28, 28, 1), activation=ACTIVATION))
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model.add(layers.MaxPooling2D())
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model.add(layers.Conv2D(64, 5, activation=ACTIVATION))
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model.add(layers.MaxPooling2D())
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model.add(layers.Flatten())
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model.add(layers.Dense(100, activation=ACTIVATION))
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model.add(layers.Dense(10, activation="softmax"))
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model.compile(loss="categorical_crossentropy", metrics=['accuracy'])
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model.fit(X_train, Y_train, batch_size=100, epochs=5)
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score = model.evaluate(X_test, Y_test, verbose=0)
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print("Test loss:", score[0])
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print("Test accuracy:", score[1])
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dg = ConvGraph(model)
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X = []
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for i in range(number_of_classes):
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X.append(np.expand_dims(X_train[np.where(y_train == i)[0][0]], axis=0))
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dg.render(X, filename='test_input_mnist')
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def test_conv_animate():
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
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X_train = X_train.astype('float32')
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X_train /= 255
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X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
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X_test = X_test.astype('float32')
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X_test /= 255
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number_of_classes = 10
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Y_train = utils.to_categorical(y_train, number_of_classes)
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Y_test = utils.to_categorical(y_test, number_of_classes)
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ACTIVATION = "relu"
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model = keras.models.Sequential()
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model.add(layers.Conv2D(32, 5, input_shape=(28, 28, 1), activation=ACTIVATION))
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model.add(layers.MaxPooling2D())
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model.add(layers.Conv2D(64, 5, activation=ACTIVATION))
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model.add(layers.MaxPooling2D())
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model.add(layers.Flatten())
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model.add(layers.Dense(100, activation=ACTIVATION))
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model.add(layers.Dense(10, activation="softmax"))
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model.compile(loss="categorical_crossentropy", metrics=['accuracy'])
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model.fit(X_train, Y_train, batch_size=100, epochs=5)
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score = model.evaluate(X_test, Y_test, verbose=0)
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print("Test loss:", score[0])
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print("Test accuracy:", score[1])
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dg = ConvGraph(model)
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X = []
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for i in range(min(50, len(np.where(y_train == 0)[0]))):
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X.append(np.expand_dims(X_train[np.where(y_train == 0)[0][i]], axis=0))
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dg.animate(X, filename='test_animate_mnist')

‎tests/test_viz.py ‎tests/test_dense.py

+2-2
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@@ -35,7 +35,7 @@ def test_dense_input_xor():
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[1, 1]])
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Y = np.array([x[0] ^ x[1] for x in X])
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model.fit(X, Y, batch_size=4, epochs=1000)
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model.fit(X, Y, batch_size=4, epochs=100)
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colors = np.array(['b', 'g'])
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fig, ax = plt.subplots()
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X = np.array(t)
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Y = np.array([1 if x[0] - x[1] >= 0 else 0 for x in X])
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model.fit(X, Y, batch_size=50, epochs=100)
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model.fit(X, Y, batch_size=50, epochs=10)
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# see which nodes activate for a given class
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X0 = X[X[:, 0] - X[:, 1] <= 0]

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