Commit 43d7e709 authored by Jakob Overgaard's avatar Jakob Overgaard
Browse files

heat map not working

parent fa1cdbb4
File added
Rroot"_tf_keras_sequential*R{"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 180, 180, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "rescaling_1_input"}}, {"class_name": "Rescaling", "config": {"name": "rescaling_1", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 180, 180, 3]}, "dtype": "float32", "scale": 0.00392156862745098, "offset": 0.0}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": 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{"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 15}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 16}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 17}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 3, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 18}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 19}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 20}]}}, "training_config": {"loss": {"class_name": "SparseCategoricalCrossentropy", "config": {"reduction": "auto", "name": "sparse_categorical_crossentropy", "from_logits": true}, "shared_object_id": 22}, "metrics": [[{"class_name": "MeanMetricWrapper", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 23}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "Adam", "config": {"name": "Adam", "learning_rate": 0.0010000000474974513, "decay": 0.0, "beta_1": 0.8999999761581421, "beta_2": 0.9990000128746033, "epsilon": 1e-07, "amsgrad": false}}}}2
 root.layer-0"_tf_keras_layer*{"name": "rescaling_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": {"class_name": "__tuple__", "items": [null, 180, 180, 3]}, "stateful": false, "must_restore_from_config": false, "class_name": "Rescaling", "config": {"name": "rescaling_1", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 180, 180, 3]}, "dtype": "float32", "scale": 0.00392156862745098, "offset": 0.0}, "shared_object_id": 1}2
root.layer_with_weights-0"_tf_keras_layer* {"name": "conv2d", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 4, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 3}}, "shared_object_id": 24}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 180, 180, 3]}}2
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root.layer_with_weights-1"_tf_keras_layer* {"name": "conv2d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 6}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 7}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 8, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 16}}, "shared_object_id": 26}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 90, 90, 16]}}2
 root.layer-4"_tf_keras_layer*{"name": "max_pooling2d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "shared_object_id": 9, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 27}}2
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 root.layer-6"_tf_keras_layer*{"name": "maxPool2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "maxPool2", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [1, 1]}, "data_format": "channels_last"}, "shared_object_id": 13, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 29}}2
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root.keras_api.metrics.0"_tf_keras_metric*{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 33}2
root.keras_api.metrics.1"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 23}2
\ No newline at end of file
......@@ -70,6 +70,8 @@ print(np.min(first_image), np.max(first_image))
num_classes = 3
model = Sequential([
layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
layers.Conv2D(16, 3, padding='same', activation='relu'),
......@@ -77,23 +79,26 @@ model = Sequential([
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.MaxPooling2D(1, name='maxPool2'),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(num_classes)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True),
metrics=['accuracy'])
epochs = 10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
outputs = [layer.output for layer in model.layers]
model.save("../../models/version1")
......
from keras_preprocessing import image
import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras.preprocessing.image import load_img
# Display
from IPython.display import Image, display
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.image as mpimg
import pathlib
data_dir = pathlib.Path("../../data/")
model_builder = keras.applications.xception.Xception
img_size = (299, 299)
......@@ -15,9 +20,72 @@ decode_predictions = keras.applications.xception.decode_predictions
last_conv_layer_name = "block14_sepconv2_act"
# The local path to our target image
img_path = keras.utils.get_file(
"test-tree.jpg",
)
img = mpimg.imread(data_dir/"test-tree.jpg")
img_path = img
display(Image(img_path))
\ No newline at end of file
imgPlot = plt.imshow(img)
plt.show()
def get_img_array(img_path, size):
# `img` is a PIL image of size 299x299
img = keras.preprocessing.image.load_img(img_path, target_size=size)
# `array` is a float32 Numpy array of shape (299, 299, 3)
array = keras.preprocessing.image.img_to_array(img)
# We add a dimension to transform our array into a "batch"
# of size (1, 299, 299, 3)
array = np.expand_dims(array, axis=0)
return array
def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):
# First, we create a model that maps the input image to the activations
# of the last conv layer as well as the output predictions
grad_model = tf.keras.models.Model(
[model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
)
# Then, we compute the gradient of the top predicted class for our input image
# with respect to the activations of the last conv layer
with tf.GradientTape() as tape:
last_conv_layer_output, preds = grad_model(img_array)
if pred_index is None:
pred_index = tf.argmax(preds[0])
class_channel = preds[:, pred_index]
# This is the gradient of the output neuron (top predicted or chosen)
# with regard to the output feature map of the last conv layer
grads = tape.gradient(class_channel, last_conv_layer_output)
# This is a vector where each entry is the mean intensity of the gradient
# over a specific feature map channel
pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
# We multiply each channel in the feature map array
# by "how important this channel is" with regard to the top predicted class
# then sum all the channels to obtain the heatmap class activation
last_conv_layer_output = last_conv_layer_output[0]
heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
heatmap = tf.squeeze(heatmap)
# For visualization purpose, we will also normalize the heatmap between 0 & 1
heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
return heatmap.numpy()
# Prepare image
img_array = preprocess_input(get_img_array(img_path, size=img_size))
# Make model
model = model_builder(weights="imagenet")
# Remove last layer's softmax
model.layers[-1].activation = None
# Print what the top predicted class is
preds = model.predict(img_array)
print("Predicted:", decode_predictions(preds, top=1)[0])
# Generate class activation heatmap
heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name)
# Display heatmap
plt.matshow(heatmap)
plt.show()
\ No newline at end of file
import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow import keras
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.inception_v3 import preprocess_input, decode_predictions
import numpy as np
import os
import matplotlib.pyplot as plt
import cv2
import pathlib
model_dir = pathlib.Path("../../models/version1")
data_dir = pathlib.Path("../../data/")
model = model_dir
ORIGINAL = (data_dir/"test-tree.jpg")
DIM = 299
img = image.load_img(ORIGINAL, target_size=(DIM, DIM))
imgPlot = plt.imshow(img)
plt.show()
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
with tf.GradientTape() as tape:
last_conv_layer = model.get_layer('conv2d_93')
iterate = tf.keras.models.Model([model.inputs], [model.output, last_conv_layer.output])
model_out, last_conv_layer = iterate(x)
class_out = model_out[:, np.argmax(model_out[0])]
grads = tape.gradient(class_out, last_conv_layer)
pooled_grads = K.mean(grads, axis=(0, 1, 2))
heatmap = tf.reduce_mean(tf.multiply(pooled_grads, last_conv_layer), axis=-1)
heatmap = np.maximum(heatmap, 0)
heatmap /= np.max(heatmap)
print(heatmap.shape)
heatmap = heatmap.reshape((8, 8))
plt.matshow(heatmap)
plt.show()
img = plt.imread(ORIGINAL)
INTENSITY = 0.5
heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
heatmap = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET)
img = heatmap * INTENSITY + img
imgPlot = plt.imshow(img)
plt.show()
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