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copy_of_sign_language_mnist_dataset_trained.py
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copy_of_sign_language_mnist_dataset_trained.py
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# -*- coding: utf-8 -*-
"""Copy of sign-language-mnist-dataset-trained.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1TKNx7JJpu02yYjJFGTPbZFsSknT7YPQo
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from google.colab import drive
drive.mount('/content/gdrive')
ls
train = pd.read_csv('asl_data_train/sign-language-mnist/sign-mnist-train.csv')
test = pd.read_csv('asl_data_train/sign-language-mnist/sign-mnist-test.csv')
ls
train.head()
train.shape
labels = train['label'].values
unique_val = np.array(labels)
np.unique(unique_val)
plt.figure(figsize = (18,8))
sns.countplot(x =labels)
train.drop('label', axis = 1, inplace = True)
images = train.values
images = np.array([np.reshape(i, (28, 28)) for i in images])
images = np.array([i.flatten() for i in images])
from sklearn.preprocessing import LabelBinarizer
label_binrizer = LabelBinarizer()
labels = label_binrizer.fit_transform(labels)
plt.imshow(images[0].reshape(28,28))
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(images, labels, test_size = 0.3, random_state = 101)
import keras
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout
batch_size = 128
num_classes = 24
epochs = 50
x_train = x_train / 255
x_test = x_test / 255
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
plt.imshow(x_train[0].reshape(28,28))
model = Sequential()
model.add(Conv2D(64, kernel_size=(3,3), activation = 'relu', input_shape=(28,28,1) ))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Conv2D(64, kernel_size = (3, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Conv2D(64, kernel_size = (3, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Flatten())
model.add(Dense(128, activation = 'relu'))
model.add(Dropout(0.20))
model.add(Dense(num_classes, activation = 'softmax'))
model.compile(loss = keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(),
metrics=['accuracy'])
history = model.fit(x_train, y_train, validation_data = (x_test, y_test), epochs=epochs, batch_size=batch_size)
model.save("testmodel3.h5")
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title("Accuracy")
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.legend(['train','test'])
plt.show()
test_labels = test['label']
test.drop('label', axis = 1, inplace = True)
test_images = test.values
test_images = np.array([np.reshape(i, (28, 28)) for i in test_images])
test_images = np.array([i.flatten() for i in test_images])
test_labels = label_binrizer.fit_transform(test_labels)
test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)
test_images.shape
y_pred = model.predict(test_images)
from sklearn.metrics import accuracy_score
accuracy_score(test_labels, y_pred.round())
from keras.models import load_model
from keras.preprocessing import image
model = load_model("testmodel3.h5")
#test_image = image.load_img('asl_data_train/a.png',color_mode="grayscale",target_size=(28,28,1))
test_image = x_train[3]
plt.imshow(x_train[3].reshape(28,28))
#print(test_image.format)
#print(test_image.mode)
#print(test_image.size)
test_image = image.img_to_array(test_image)
test_image = test_image.reshape((-1,) + test_image.shape)
print(test_image.dtype)
print(test_image.shape)
y_pred = model.predict_classes(test_image)
print(y_pred)
prediction = y_pred[0]
classname = y_pred[0]
print("Class: ",classname)
print(y_pred)
#test_image = image.load_img('a.jpg',color_mode="grayscale",target_size=(28,28,1))
img = image.load_img(('a.jpg') , target_size=(32,32,3))
img = image.img_to_array(img)
img = img.reshape((1,) + img.shape)
# img = img/255
img = img.reshape(28,28,-1)
img_class=model.predict_classes(img)
# this model above was already trained
# code from https://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-#neural-networks-python-keras/
prediction = img_class[0]
classname = img_class[0]
print("Class: ",classname)