Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
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4.99k
26/26 [==============================] - 8s 316ms/step - loss: 1.0001 - accuracy: 0.6206 - val_loss: 2.0375 - val_accuracy: 0.2834
Epoch 6/30
26/26 [==============================] - 8s 315ms/step - loss: 0.9602 - accuracy: 0.6355 - val_loss: 1.4412 - val_accuracy: 0.3978
Epoch 7/30
26/26 [==============================] - 8s 316ms/step - loss: 0.9418 - accuracy: 0.6461 - val_loss: 1.5257 - val_accuracy: 0.4305
Epoch 8/30
26/26 [==============================] - 8s 316ms/step - loss: 0.8911 - accuracy: 0.6649 - val_loss: 1.1530 - val_accuracy: 0.5858
Epoch 9/30
26/26 [==============================] - 8s 316ms/step - loss: 0.8834 - accuracy: 0.6694 - val_loss: 1.2026 - val_accuracy: 0.5531
Epoch 10/30
26/26 [==============================] - 8s 316ms/step - loss: 0.8752 - accuracy: 0.6724 - val_loss: 1.4917 - val_accuracy: 0.5695
Epoch 11/30
26/26 [==============================] - 8s 316ms/step - loss: 0.8690 - accuracy: 0.6594 - val_loss: 1.4115 - val_accuracy: 0.6022
Epoch 12/30
26/26 [==============================] - 8s 314ms/step - loss: 0.8586 - accuracy: 0.6761 - val_loss: 1.0692 - val_accuracy: 0.6349
Epoch 13/30
26/26 [==============================] - 8s 315ms/step - loss: 0.8120 - accuracy: 0.6894 - val_loss: 1.5233 - val_accuracy: 0.6567
Epoch 14/30
26/26 [==============================] - 8s 316ms/step - loss: 0.8275 - accuracy: 0.6857 - val_loss: 1.9079 - val_accuracy: 0.5804
Epoch 15/30
26/26 [==============================] - 8s 316ms/step - loss: 0.7624 - accuracy: 0.7127 - val_loss: 0.9543 - val_accuracy: 0.6540
Epoch 16/30
26/26 [==============================] - 8s 315ms/step - loss: 0.7595 - accuracy: 0.7266 - val_loss: 4.5757 - val_accuracy: 0.4877
Epoch 17/30
26/26 [==============================] - 8s 315ms/step - loss: 0.7577 - accuracy: 0.7154 - val_loss: 1.8411 - val_accuracy: 0.5749
Epoch 18/30
26/26 [==============================] - 8s 316ms/step - loss: 0.7596 - accuracy: 0.7163 - val_loss: 1.0660 - val_accuracy: 0.6703
Epoch 19/30
26/26 [==============================] - 8s 315ms/step - loss: 0.7492 - accuracy: 0.7160 - val_loss: 1.2462 - val_accuracy: 0.6485
Epoch 20/30
26/26 [==============================] - 8s 315ms/step - loss: 0.7269 - accuracy: 0.7330 - val_loss: 5.8287 - val_accuracy: 0.3379
Epoch 21/30
26/26 [==============================] - 8s 315ms/step - loss: 0.7193 - accuracy: 0.7275 - val_loss: 4.7058 - val_accuracy: 0.6049
Epoch 22/30
26/26 [==============================] - 8s 316ms/step - loss: 0.7251 - accuracy: 0.7318 - val_loss: 1.5608 - val_accuracy: 0.6485
Epoch 23/30
26/26 [==============================] - 8s 314ms/step - loss: 0.6888 - accuracy: 0.7466 - val_loss: 1.7914 - val_accuracy: 0.6240
Epoch 24/30
26/26 [==============================] - 8s 314ms/step - loss: 0.7051 - accuracy: 0.7339 - val_loss: 2.0918 - val_accuracy: 0.6158
Epoch 25/30
26/26 [==============================] - 8s 315ms/step - loss: 0.6920 - accuracy: 0.7454 - val_loss: 0.7284 - val_accuracy: 0.7575
Epoch 26/30
26/26 [==============================] - 8s 316ms/step - loss: 0.6502 - accuracy: 0.7523 - val_loss: 2.5474 - val_accuracy: 0.5313
Epoch 27/30
26/26 [==============================] - 8s 315ms/step - loss: 0.7101 - accuracy: 0.7330 - val_loss: 26.8117 - val_accuracy: 0.3297
Epoch 28/30
26/26 [==============================] - 8s 315ms/step - loss: 0.6632 - accuracy: 0.7548 - val_loss: 20.1011 - val_accuracy: 0.3243
Epoch 29/30
26/26 [==============================] - 8s 315ms/step - loss: 0.6682 - accuracy: 0.7505 - val_loss: 11.5872 - val_accuracy: 0.3297
Epoch 30/30
26/26 [==============================] - 8s 315ms/step - loss: 0.6758 - accuracy: 0.7514 - val_loss: 5.7229 - val_accuracy: 0.4305
3/3 [==============================] - 0s 95ms/step - loss: 5.7229 - accuracy: 0.4305
Top-1 accuracy on the validation set: 43.05%.
As we can notice from the above cells, FixRes leads to a better performance. Another advantage of FixRes is the improved total training time and reduction in GPU memory usage. FixRes is model-agnostic, you can use it on any image classification model to potentially boost performance.
You can find more results here that were gathered by running the same code with different random seeds.
How to obtain a class activation heatmap for an image classification model.
Adapted from Deep Learning with Python (2017).
Setup
import numpy as np
import tensorflow as tf
from tensorflow import keras
# Display
from IPython.display import Image, display
import matplotlib.pyplot as plt
import matplotlib.cm as cm
Configurable parameters
You can change these to another model.
To get the values for last_conv_layer_name use model.summary() to see the names of all layers in the model.
model_builder = keras.applications.xception.Xception
img_size = (299, 299)
preprocess_input = keras.applications.xception.preprocess_input
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(
\"african_elephant.jpg\", \"https://i.imgur.com/Bvro0YD.png\"
)
display(Image(img_path))
jpeg
The Grad-CAM algorithm
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