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July 26, 2018 02:11
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# A very simple perceptron for classifying american sign language letters | |
import signdata | |
import numpy as np | |
import keras | |
from keras.models import Sequential | |
from keras.layers import Dense, Flatten, Dropout, BatchNormalization, Conv2D, MaxPooling2D | |
from keras.utils import np_utils | |
import wandb | |
from wandb.keras import WandbCallback | |
# logging code | |
run = wandb.init() | |
config = run.config | |
config.team_name = "teambob" | |
config.loss = "categorical_crossentropy" | |
config.optimizer = "adam" | |
config.epochs = 10 | |
input_shape = (28, 28, 1) | |
if (config.team_name == 'default'): | |
raise ValueError("Please set config.team_name to be your team name") | |
# load data | |
(X_test, y_test) = signdata.load_test_data() | |
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1) | |
(X_train, y_train) = signdata.load_train_data() | |
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1) | |
img_width = X_test.shape[1] | |
img_height = X_test.shape[2] | |
# one hot encode outputs | |
y_train = np_utils.to_categorical(y_train) | |
y_test = np_utils.to_categorical(y_test) | |
num_classes = y_train.shape[1] | |
# you may want to normalize the data here.. | |
# create model | |
model = Sequential() | |
model.add(Conv2D(32, kernel_size=(3, 3), | |
activation='relu', | |
input_shape=input_shape)) | |
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(num_classes, activation='softmax')) | |
model.compile(loss=keras.losses.categorical_crossentropy, | |
optimizer=keras.optimizers.Adadelta(), | |
metrics=['accuracy']) | |
# Fit the model | |
model.fit(X_train, y_train, epochs=config.epochs, validation_data=(X_test, y_test), | |
callbacks=[WandbCallback(data_type="image", labels=signdata.letters)]) |
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