App MNIST

Primer App

app.py

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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)