import numpy as np
from sklearn.metrics import accuracy_score
from matplotlib import pyplot as plt
import tensorflow as tf
print("We're using TF", tf.__version__)
import sys
sys.path.append("../..")
import keras_utils
import preprocessed_mnist
X_train, y_train, X_val, y_val, X_test, y_test = preprocessed_mnist.load_dataset(
)
# X contains rgb values divided by 255
print("X_train [shape %s] sample patch:\n" % (str(X_train.shape)),
X_train[1, 15:20, 5:10])
print("A closeup of a sample patch:")
plt.imshow(X_train[1, 15:20, 5:10], cmap="Greys")
plt.show()
print("And the whole sample:")
plt.imshow(X_train[1], cmap="Greys")
plt.show()
print("y_train [shape %s] 10 samples:\n" % (str(y_train.shape)), y_train[:10])
X_train_flat = X_train.reshape((X_train.shape[0], -1))
print(X_train_flat.shape)
X_val_flat = X_val.reshape((X_val.shape[0], -1))
print(X_val_flat.shape)
import keras
# ``` # # That will create all the necessary variables automatically. # Here you can also choose an activation function (remember that we need it for a hidden layer!). # # Now define the MLP with 2 hidden layers and restart training with the cell above. # # You're aiming for ~0.97 validation accuracy here. # In[ ]: # write the code here to get a new `step` operation and then run the cell with training loop above. # name your variables in the same way (e.g. logits, probas, classes, etc) for safety. ### YOUR CODE HERE ### from preprocessed_mnist import load_dataset X_train, y_train, X_val, y_val, X_test, y_test = load_dataset() print(X_train.shape, y_train.shape) # In[ ]: s = reset_tf_session() input_X = tf.placeholder(tf.float32, shape=(None, 784)) input_y = tf.placeholder(tf.float32, shape=(None, 10)) hidden1 = tf.layers.dense(input_X, 256, activation=tf.nn.sigmoid) hidden2 = tf.layers.dense(hidden1, 256, activation=tf.nn.sigmoid) logits = tf.layers.dense(hidden2, 10) probas = tf.nn.softmax(logits) classes = tf.argmax(probas, 1) #print(hidden1.shape,logits.shape, probas.shape, classes.shape) loss = tf.reduce_mean(
xentropy = -logits_for_answers + np.log(np.sum(np.exp(logits), axis=-1))
return xentropy
def grad_softmax_crossentropy_with_logits(logits, reference_answers):
"""Compute crossentropy gradient from logits[batch,n_classes] and ids of correct answers"""
ones_for_answers = np.zeros_like(logits)
ones_for_answers[np.arange(len(logits)), reference_answers] = 1
softmax = np.exp(logits) / np.exp(logits).sum(axis=-1, keepdims=True)
return (-ones_for_answers + softmax) / logits.shape[0]
X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(flatten=True)
plt.figure(figsize=[6, 6])
for i in range(3):
plt.subplot(2, 2, i + 1)
plt.title("Label: %i" % y_train[i])
plt.imshow(X_train[i].reshape([28, 28]), cmap='gray')
network = []
network.append(Dense(X_train.shape[1], 100))
network.append(ReLU())
network.append(Dense(100, 200))
network.append(ReLU())
network.append(Dense(200, 100))
network.append(ReLU())
network.append(Dense(100, 10))
from preprocessed_mnist import load_dataset
# load data from keras
X_orig_train, y_orig_train, X_orig_val, y_orig_val, X_orig_test, y_orig_test = load_dataset(
)
import numpy as np
import tensorflow as tf
def preprocess():
"""
flatten pixels to vector, transform y to one-hot-encoding form
"""
X_train = X_orig_train.reshape(X_orig_train.shape[0], -1).T
X_val = X_orig_val.reshape(X_orig_val.shape[0], -1).T
X_test = X_orig_test.reshape(X_orig_test.shape[0], -1).T
y_train = np.eye(10)[y_orig_train.reshape(-1)].T
y_val = np.eye(10)[y_orig_val.reshape(-1)].T
y_test = np.eye(10)[y_orig_test.reshape(-1)].T
return X_train, y_train, X_val, y_val, X_test, y_test
X_train, y_train, X_val, y_val, X_test, y_test = preprocess()
print(X_train.shape, y_train.shape)
# random seed, so that the result will be consistent
seed = 10
# one hidden layer , and the number of units is 300
hidden_units = 300
