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minor changes to scripts

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This commit is contained in:
Abubakar Abid 2019-07-25 14:14:32 -07:00
parent 2b2d59f804
commit e1b25b2901
2 changed files with 40 additions and 51 deletions
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54
examples/mnist.py
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@ -3,45 +3,33 @@ import sys
sys.path.insert(1, '../gradio')
import gradio
from tensorflow.keras.layers import *
from tensorflow.keras import backend as K
(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train, x_test = x_train.reshape(-1,784) / 255.0, x_test.reshape(-1,784) / 255.0
model = tf.keras.models.Sequential()
model.add(Reshape((28, 28, 1), input_shape=(784,)))
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train[:100], y_train[:100], epochs=1)
def get_trained_model(n):
model = tf.keras.models.Sequential()
model.add(Reshape((28, 28, 1), input_shape=(784,)))
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train[:n], y_train[:n], epochs=1)
return model
def saliency(interface, model, input, processed_input, output, processed_output):
with interface.graph.as_default():
with interface.sess.as_default():
output = output.argmax()
input_tensors = [model.inputs[0], K.learning_phase()]
saliency_input = model.layers[0].input
saliency_output = model.layers[-1].output[:, output]
gradients = model.optimizer.get_gradients(saliency_output, saliency_input)
compute_gradients = K.function(inputs=input_tensors, outputs=gradients)
saliency_graph = compute_gradients(processed_input.reshape(-1, 784))
normalized_saliency = (abs(saliency_graph[0]) - abs(saliency_graph[0]).min()) / \
(abs(saliency_graph[0]).max() - abs(saliency_graph[0]).min())
return normalized_saliency.reshape(28, 28)
model = get_trained_model(n=100)
# Gradio code #
sketchpad = gradio.inputs.Sketchpad(flatten=True, sample_inputs=x_test[:10])
io = gradio.Interface(inputs=sketchpad, outputs="label", model=model, model_type="keras", verbose=False)
httpd, path_to_local_server, share_url = io.launch(inline=True, share=True, inbrowser=True)
sketchpad = gradio.inputs.Sketchpad(flatten=True)
iface = gradio.Interface(inputs=sketchpad, outputs="label", model=model, model_type='keras')
httpd, path_to_local_server, share_url = iface.launch(inline=True, share=True)
print("MNIST model without saliency: ", share_url)
print("URL for MNIST model interface: ", share_url)

37
examples/mnist_saliency.py
View File

@ -8,21 +8,21 @@ from tensorflow.keras import backend as K
(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train, x_test = x_train.reshape(-1,784) / 255.0, x_test.reshape(-1,784) / 255.0
model = tf.keras.models.Sequential()
model.add(Reshape((28, 28, 1), input_shape=(784,)))
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=1)
def get_trained_model(n):
model = tf.keras.models.Sequential()
model.add(Reshape((28, 28, 1), input_shape=(784,)))
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train[:n], y_train[:n], epochs=1)
return model
def saliency(interface, model, input, processed_input, output, processed_output):
@ -40,8 +40,9 @@ def saliency(interface, model, input, processed_input, output, processed_output)
return normalized_saliency.reshape(28, 28)
sketchpad = gradio.inputs.Sketchpad(flatten=True, sample_inputs=x_train[:10])
iface = gradio.Interface(inputs=sketchpad, outputs="label", model=model, model_type='keras', saliency=saliency)
httpd, path_to_local_server, share_url = iface.launch(inline=True, share=True)
model = get_trained_model(n=100)
sketchpad = gradio.inputs.Sketchpad(flatten=True, sample_inputs=x_test[:10])
io = gradio.Interface(inputs=sketchpad, outputs="label", model=model, model_type="keras", saliency=saliency)
httpd, path_to_local_server, share_url = io.launch(inline=True, share=True, inbrowser=True)
print("MNIST model with saliency: ", share_url)
print("URL for MNIST model interface with saliency:", share_url)