Add files via upload

Author: bnsreenu

152-visualizing_conv_layer_outputs.py

@@ -0,0 +1,124 @@
+
+# https://youtu.be/ho6JXE3EbZ8
+"""
+@author: Sreenivas Bhattiprolu
+
+Copying VGG16 architecture and picking the conv layers of interest 
+to generate filtered responses. 
+"""
+
+from keras.models import Sequential
+from keras.layers.core import Flatten, Dense, Dropout
+from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
+from keras.optimizers import SGD
+import numpy as np
+from matplotlib import pyplot as plt
+from keras.models import Model
+  
+model = Sequential()
+model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))
+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'))
+
+sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
+model.compile(optimizer=sgd, loss='categorical_crossentropy')
+
+print(model.summary())  
+    
+ 
+#Understand the filters in the model 
+#Let us pick the first hidden layer as the layer of interest.
+layer = model.layers #Conv layers at 1, 3, 6, 8, 11, 13, 15
+filters, biases = model.layers[1].get_weights()
+print(layer[1].name, filters.shape)
+
+   
+# plot filters
+
+fig1=plt.figure(figsize=(8, 12))
+columns = 8
+rows = 8
+n_filters = columns * rows
+for i in range(1, n_filters +1):
+    f = filters[:, :, :, i-1]
+    fig1 =plt.subplot(rows, columns, i)
+    fig1.set_xticks([])  #Turn off axis
+    fig1.set_yticks([])
+    plt.imshow(f[:, :, 0], cmap='gray') #Show only the filters from 0th channel (R)
+    #ix += 1
+plt.show()    
+
+#### Now plot filter outputs    
+
+#Define a new truncated model to only include the conv layers of interest
+#conv_layer_index = [1, 3, 6, 8, 11, 13, 15]
+conv_layer_index = [1, 3, 6]  #TO define a shorter model
+outputs = [model.layers[i].output for i in conv_layer_index]
+model_short = Model(inputs=model.inputs, outputs=outputs)
+print(model_short.summary())
+
+#Input shape to the model is 224 x 224. SO resize input image to this shape.
+from keras.preprocessing.image import load_img, img_to_array
+img = load_img('monalisa.jpg', target_size=(224, 224)) #VGG user 224 as input
+
+# convert the image to an array
+img = img_to_array(img)
+# expand dimensions to match the shape of model input
+img = np.expand_dims(img, axis=0)
+
+# Generate feature output by predicting on the input image
+feature_output = model_short.predict(img)
+
+
+columns = 8
+rows = 8
+for ftr in feature_output:
+    #pos = 1
+    fig=plt.figure(figsize=(12, 12))
+    for i in range(1, columns*rows +1):
+        fig =plt.subplot(rows, columns, i)
+        fig.set_xticks([])  #Turn off axis
+        fig.set_yticks([])
+        plt.imshow(ftr[0, :, :, i-1], cmap='gray')
+        #pos += 1
+    plt.show()
+