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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) |