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sarincrsarincr
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MNIST
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Diff for: ‎23.Tensorflow Basics.ipynb

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"source": [
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"sess.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_1 = tf.placeholder(tf.float32, name = \"X_1\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_2 = tf.placeholder(tf.float32, name = \"X_2\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"multiply = tf.multiply(X_1, X_2, name = \"multiply\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 4. 10. 18.]\n"
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]
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}
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],
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"source": [
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"with tf.Session() as session:\n",
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" result = session.run(multiply, feed_dict={X_1:[1,2,3], X_2:[4,5,6]})\n",
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" print(result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 5 12 21 32]\n"
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]
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}
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],
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"source": [
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"# Import `tensorflow`\n",
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"import tensorflow as tf\n",
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"\n",
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"# Initialize two constants\n",
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"x1 = tf.constant([1,2,3,4])\n",
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"x2 = tf.constant([5,6,7,8])\n",
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"\n",
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"# Multiply\n",
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"result = tf.multiply(x1, x2)\n",
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"\n",
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"# Initialize Session and run `result`\n",
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"with tf.Session() as sess:\n",
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" output = sess.run(result)\n",
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" print(output)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"matrix1 = np.array([(2,2,2),(2,2,2),(2,2,2)],dtype = 'int32')\n",
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"matrix2 = np.array([(1,1,1),(1,1,1),(1,1,1)],dtype = 'int32')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[2, 2, 2],\n",
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" [2, 2, 2],\n",
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" [2, 2, 2]], dtype=int32)"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"matrix1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[1, 1, 1],\n",
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" [1, 1, 1],\n",
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" [1, 1, 1]], dtype=int32)"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"matrix2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"matrix1 = tf.constant(matrix1)\n",
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"matrix2 = tf.constant(matrix2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"matrix_product = tf.matmul(matrix1, matrix2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"matrix_sum = tf.add(matrix1,matrix2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"matrix_3 = np.array([(2,7,2),(1,4,2),(9,0,2)],dtype = 'float32')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[2. 7. 2.]\n",
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" [1. 4. 2.]\n",
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" [9. 0. 2.]]\n"
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]
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}
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],
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"source": [
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"print(matrix_3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"matrix_det = tf.matrix_determinant(matrix_3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"with tf.Session() as sess:\n",
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" result1 = sess.run(matrix_product)\n",
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" result2 = sess.run(matrix_sum)\n",
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" result3 = sess.run(matrix_det)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[6 6 6]\n",
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" [6 6 6]\n",
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" [6 6 6]]\n"
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]
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}
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],
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"source": [
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"print (result1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[3 3 3]\n",
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" [3 3 3]\n",
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" [3 3 3]]\n"
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]
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}
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],
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"source": [
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"print (result2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"55.999992\n"
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]
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}
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],
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"source": [
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"print (result3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"import numpy as np\n",
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"from tensorflow.examples.tutorials.mnist import input_data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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"def run_cnn():\n",
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" mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot = True)\n",
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" learning_rate = 0.0001\n",
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" epochs = 10\n",
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" batch_size = 50"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = tf.placeholder(tf.float32, [None, 784])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_shaped = tf.reshape(x, [-1, 28, 28, 1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"y = tf.placeholder(tf.float32, [None, 10])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = tf.constant(-2.0, name=\"i\", dtype=tf.float32)\n",
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"a = tf.constant(5.0, name=\"j\", dtype=tf.float32)\n",
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"b = tf.constant(13.0, name=\"k\", dtype=tf.float32)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"-2.0\n"
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]
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}
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],
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"source": [
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"sess = tf.Session()\n",
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"print(sess.run(x))"
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]
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}
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],
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"metadata": {

Diff for: ‎MNIST_data/t10k-images-idx3-ubyte.gz

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Diff for: ‎MNIST_data/train-images-idx3-ubyte.gz

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Diff for: ‎MNIST_data/train-labels-idx1-ubyte.gz

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