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| import tensorflow as tf | |
| from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Dropout, Add, Layer, Flatten, BatchNormalization, Activation | |
| from tensorflow.keras.models import Model | |
| class ResLayer(Layer): | |
| def __init__(self, filters, name = "Res_Layer"): | |
| super(ResLayer, self).__init__(name = name) | |
| self.filters = filters | |
| self.f1, self.f2, self.f3 = self.filters | |
| # Camino normal | |
| self.Conv_1 = Conv2D(filters = self.f1, kernel_size = (1, 1), strides = (1, 1)) | |
| self.MaxPool_1 = MaxPool2D(pool_size = (2, 2)) | |
| self.BatchNorm_1 = BatchNormalization() | |
| self.Activation_1 = Activation("relu") | |
| self.Conv_2 = Conv2D(filters = self.f2, kernel_size = (1, 1), strides = (1, 1)) | |
| self.BatchNorm_2 = BatchNormalization() | |
| self.Activation_2 = Activation("relu") | |
| self.Conv_3 = Conv2D(filters = self.f3, kernel_size = (1, 1), strides = (1, 1)) | |
| self.BatchNorm_3 = BatchNormalization() | |
| # Camino corto | |
| self.Conv_4 = Conv2D(filters = self.f3, kernel_size = (1, 1), strides = (1, 1)) | |
| self.MaxPool_2 = MaxPool2D(pool_size = (2, 2)) | |
| self.Add = Add() | |
| self.Activation_3 = Activation("relu") | |
| def call(self, inputs): | |
| X_copy = inputs | |
| X = self.Conv_1(inputs) | |
| X = self.MaxPool_1(X) | |
| X = self.BatchNorm_1(X) | |
| X = self.Activation_1(X) | |
| X = self.Conv_2(X) | |
| X = self.BatchNorm_2(X) | |
| X = self.Activation_2(X) | |
| X = self.Conv_3(X) | |
| X = self.BatchNorm_3(X) | |
| X_copy = self.Conv_4(X_copy) | |
| X_copy = self.MaxPool_2(X_copy) | |
| outputs = self.Add([X, X_copy]) | |
| outputs = self.Activation_3(outputs) | |
| return outputs | |
| class ResNet(Model): | |
| def __init__(self, filters = [[64, 128, 256]], name = "ResNet"): | |
| super(ResNet, self).__init__(name = name) | |
| self.filters = filters | |
| self.nb_layers = tf.shape(self.filters)[0].numpy() | |
| self.res_layer = [ResLayer(filters) | |
| for i, filters in enumerate(self.filters)] | |
| self.Flatten = Flatten() | |
| self.Dense_1 = Dense(units = 128, activation = "relu") | |
| self.dropout_1 = Dropout(rate = 0.2) | |
| self.Dense_2 = Dense(units = 64, activation = "relu") | |
| self.dropout_2 = Dropout(rate = 0.1) | |
| self.Dense_Out = Dense(units = 10, activation = "softmax") | |
| def call(self, inputs): | |
| outputs = inputs | |
| for i in range(self.nb_layers): | |
| outputs = self.res_layer[i](outputs) | |
| outputs = self.Flatten(outputs) | |
| outputs = self.Dense_1(outputs) | |
| outputs = self.dropout_1(outputs) | |
| outputs = self.Dense_2(outputs) | |
| outputs = self.dropout_2(outputs) | |
| outputs = self.Dense_Out(outputs) | |
| return outputs | |
| model = ResNet() | |
| model.build(input_shape = [None, 28, 28, 1]) | |
| model.load_weights("ResNet_Weights.tf") | |
| import gradio as gr | |
| def digit_recognition(img): | |
| img = img / 255. | |
| img = tf.expand_dims(img, axis = -1) | |
| img = tf.convert_to_tensor([img], dtype = tf.float32) | |
| prediction = model(img) | |
| prediction = tf.squeeze(prediction) | |
| return {"Cero": float(prediction[0]), | |
| "Uno": float(prediction[1]), | |
| "Dos": float(prediction[2]), | |
| "Tres": float(prediction[3]), | |
| "Cuatro": float(prediction[4]), | |
| "Cinco": float(prediction[5]), | |
| "Seis": float(prediction[6]), | |
| "Siete": float(prediction[7]), | |
| "Ocho": float(prediction[8]), | |
| "Nueve": float(prediction[9])} | |
| app = gr.Interface(fn = digit_recognition, inputs = "sketchpad", outputs = "label", description = "Dibuja un número", title = "MNIST Digit Recognition") | |
| app.launch(share = True) |