Feb 6 class updates

Author: vanpelt

examples/keras-cnn/wandb/settings

@@ -1,4 +1,4 @@
 [default]
 entity: qualcomm
-project: digits-dec4
+project: digits-feb6
 base_url: https://api.wandb.ai

examples/keras-mlp/wandb/settings

@@ -1,4 +1,4 @@
 [default]
 entity: qualcomm
-project: digits-sep25
+project: digits-feb6
 base_url: https://api.wandb.ai

examples/keras-perceptron/wandb/settings

@@ -1,4 +1,4 @@
 [default]
-entity: oreilly-class
-project: digits-jan21
+entity: qualcomm
+project: digits-feb6
 base_url: https://api.wandb.ai

examples/keras-transfer/vgg-inspect.py

@@ -1,82 +1,14 @@
-# pip install opencv-python
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers.core import Flatten, Dense, Dropout
-from tensorflow.keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
-from tensorflow.keras.optimizers import SGD
-from tensorflow.keras import backend as K
+from tensorflow.keras.applications.vgg16 import VGG16
 import cv2
 import numpy as np
-import os
-
-# get weights from https://drive.google.com/file/d/0Bz7KyqmuGsilT0J5dmRCM0ROVHc
-K.set_image_dim_ordering('th')
-
-
-def VGG_16(weights_path=None):
-    model = Sequential()
-    model.add(ZeroPadding2D((1, 1), input_shape=(3, 224, 224)))
-    model.add(Convolution2D(64, 3, 3, activation='relu'))
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(64, 3, 3, activation='relu'))
-    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
-
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(128, 3, 3, activation='relu'))
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(128, 3, 3, activation='relu'))
-    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
-
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(256, 3, 3, activation='relu'))
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(256, 3, 3, activation='relu'))
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(256, 3, 3, activation='relu'))
-    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
-
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(512, 3, 3, activation='relu'))
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(512, 3, 3, activation='relu'))
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(512, 3, 3, activation='relu'))
-    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
-
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(512, 3, 3, activation='relu'))
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(512, 3, 3, activation='relu'))
-    model.add(ZeroPadding2D((1, 1)))
-    model.add(Convolution2D(512, 3, 3, activation='relu'))
-    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
-
-    model.add(Flatten())
-    model.add(Dense(4096, activation='relu'))
-    model.add(Dropout(0.5))
-    model.add(Dense(4096, activation='relu'))
-    model.add(Dropout(0.5))
-    model.add(Dense(1000, activation='softmax'))
-
-    if weights_path:
-        model.load_weights(weights_path)
-
-    return model
-
 
 if __name__ == "__main__":
-    model = VGG_16()
+    model = VGG16()
     model.summary()
-    # Test pretrained model if weights exist
-    if os.path.exists("vgg16_weights.h5"):
-        im = cv2.resize(cv2.imread('elephant.jpg'),
-                        (224, 224)).astype(np.float32)
-        im[:, :, 0] -= 103.939
-        im[:, :, 1] -= 116.779
-        im[:, :, 2] -= 123.68
-        im = im.transpose((2, 0, 1))
-        im = np.expand_dims(im, axis=0)
-        model = VGG_16('vgg16_weights.h5')
-        sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
-        model.compile(optimizer=sgd, loss='categorical_crossentropy')
-        out = model.predict(im)
-        print(np.argmax(out))
+    im = cv2.resize(cv2.imread('elephant.jpg'),
+                    (224, 224)).astype(np.float32)
+    im[:, :, 0] -= 103.939
+    im[:, :, 1] -= 116.779
+    im[:, :, 2] -= 123.68
+    out = model.predict(im)
+    print(np.argmax(out))