mirror of
https://github.com/gradio-app/gradio.git
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51 lines
2.2 KiB
Python
51 lines
2.2 KiB
Python
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import tensorflow as tf
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import sys
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sys.path.insert(1, '../gradio')
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import gradio
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from tensorflow.keras.layers import *
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from tensorflow.keras import backend as K
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(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
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x_train, x_test = x_train.reshape(-1,784) / 255.0, x_test.reshape(-1,784) / 255.0
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def get_trained_model(n):
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model = tf.keras.models.Sequential()
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model.add(Reshape((28, 28, 1), input_shape=(784,)))
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model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
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model.add(Conv2D(64, (3, 3), activation='relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(128, activation='relu'))
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model.add(Dropout(0.5))
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model.add(Dense(10, activation='softmax'))
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
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model.fit(x_train[:n], y_train[:n], epochs=2)
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print(model.evaluate(x_test, y_test))
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return model
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def saliency(interface, model, input, processed_input, output, processed_output):
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with interface.graph.as_default():
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with interface.sess.as_default():
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output = output.argmax()
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input_tensors = [model.inputs[0], K.learning_phase()]
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saliency_input = model.layers[0].input
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saliency_output = model.layers[-1].output[:, output]
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gradients = model.optimizer.get_gradients(saliency_output, saliency_input)
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compute_gradients = K.function(inputs=input_tensors, outputs=gradients)
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saliency_graph = compute_gradients(processed_input.reshape(-1, 784))
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normalized_saliency = (abs(saliency_graph[0]) - abs(saliency_graph[0]).min()) / \
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(abs(saliency_graph[0]).max() - abs(saliency_graph[0]).min())
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return normalized_saliency.reshape(28, 28)
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model = get_trained_model(n=50000)
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sketchpad = gradio.inputs.Sketchpad(flatten=True, sample_inputs=x_test[:10])
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io = gradio.Interface(inputs=sketchpad, outputs="label", model=model, model_type="keras", saliency=saliency, always_flag=True, interactivity_disabled=True)
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httpd, path_to_local_server, share_url = io.launch(inline=True, share=True, inbrowser=True)
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print("URL for MNIST model interface with saliency:", share_url)
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