1_tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.11.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import basecaller\n",
    "import numpy as np\n",
    "import csv\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "this function provides information about the array    \n",
    "\"\"\"\n",
    "def array_inspect(x):\n",
    "    print (\"Shape is\",(x.shape))\n",
    "    print((\"Length is\",len(x)))\n",
    "    print((\"Dimension is\",x.ndim))\n",
    "    print((\"Total Size is\",x.size))\n",
    "    print((\"Type is\",x.dtype))\n",
    "    print((\"Type Name is\",x.dtype.name))\n",
    "    #print((\"Mean is\",(x).mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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'0001', '0000', '2300', '1220', '1031', '2000', '3003', '1221', '3030', '2113', '0001', '3013', '0111', '1103', '0003', '1222', '0211', '2322', '2111', '2131', '2211', '0130', '0222', '3311', '1221', '2021', '2222', '0211', '3230', '2010', '2211', '2121', '2213', '2221', '1121', '1221', '2121', '3322', '0122', '2203', '1222', '1221', '0202', '2022', '0321', '1121', '3212', '2112', '0020', '3300', '2033', '2110', '3122', '0132', '1311', '3330', '2331', '3120', '3120', '1122', '1333', '3132', '1021', '1213', '2103', '0013', '3112', '2031', '3213', '0230', '0202', '1333', '0100', '1031', '3130', '1010', '2213', '1112', '2203', '3001', '2220', '3302', '1221', '0201', '2022', '2013', '2031', '2003', '3003', '0003', '0331', '1332', '1333', '2013', '3133', '2120', '1322', '1010', '0122', '2032', '0211', '1333', '0332', '2003', '3033', '3133', '0120', '2113', '0331', '2033', '0321', '2303', '0220', '0221', '3322', '0020', '1231', '3000', '2212', '0121', '2102', '1000', '0010', '2000', '0110', '3213', '3323', '1331', '3320', '2023', '2213', '2001', '2220', '3210', '2113', '3303', '3213', '0332', '0330', '3001', '3321', '1300', '2213', '3303', '3203', '0331', '1320', '3130', '3210', '1201', '3220', '1132', '0212', '2031', '2133', '0230', '2003', '0323', '0002', '1223', '1212', '0000', '0332', '2333', '1323', '0133', '2120', '1122', '1310', '0122', '2032', '0211', '1333', '0332', '1303', '3203', '3133', '3120', '2213', '0331', '2033', '0311', '1303', '0220', '3211', '3322', '0020', '1302', '3002', '2222', '0121', '2021', '0000', '0102', '0200', '0110', '3220', '3313', '1331', '3320', '3313', '2113', '1001', '2220', '3210', '2113', '3303', '3213', '0332', '0330', '3001', '2021', '1300', '2210', '3303', '3203', '0131', '1320', '3130', '3223', '0301', '3323', '2201', '1320', '2112', '0322', '1333', '1302', '0303', '1303', '0212', '2012', '1200', '0003', '3000', '3000', '3033', '1301', '3103', '2030', '3003', '3333', '1202', '0333', '1202', '1322', '0133', '0213', '0020', '1113', '1312', '2221', '3222', '2200', '1110', '2013', '1130', '0022', '0003', '1033', '0033', '1001', '3310', '1133', '3003', '1333', '0332', '0011', '0312', '0130', '3331', '0303', '2212', '0130', '0131', '3021', '3033', '2221', '1010', '0032', '0022', '2203', '0303', '2321', '2111', '2202', '2313', '1303', '3010', '2020', '0300', '3313', '3133', '2201', '2232', '0022', '1300', '0303', '0322', '2120', '2132', '2022', '1302', '2013', '2230', '2020', '2130', '1212', '0332', '1222', '0322', '1220', '3002', '2013', '0300', '0200', '0111', '1101', '1311', '0010', '0302', '0201', '2203', '1213', '1333', '0211', '1313', '0103', '0032', '1130', '0210', '0302', '1312', '0131', '2213', '3211', '1303', '1112', '1111', '1211', '1112', '1111', '1303', '0133', '0312', '1212', '1113', '1102', '2203', '3213', '2031', '3203', '1332', '0200', '1212', '3021', '3021', '3200', '2212', '2322', '1222', '0331', '1200', '3130', '1112', '1332', '1001', '1111', '3101', '1111', '0310', '3030', '2003', '3311', '1202', '2333', '1133', '2203', '3213', '3121', '2300', '2122', '1302', '2110', '0333', '0121', '3222', '3221', '2332', '2122', '2000', '3132', '1033', '0110', '2033', '2313', '0322', '0220', '2022', '1332', '2103', '3211', '1130', '3211', '2000', '1030', '2111', '1100', '0021', '3003', '3203', '0032', '0313', '3211', '3030', '3130', '3030', '3130', '3111', '2030', '0301', '3111', '2121', '1300', '3020', '3103', '0102', '1133', '1133', '1300', '1121', '1012', '2011', '2132', '0011', '2131', '0121', '1110', '0013', '0032', '0111', '0332', '2311', '1020', '1120', '0231', '1031', '3113', '0301', '3130', '3310', '2101', '1210', '1110', '1101', '3210', '3113', '1213', '0010', '2311', '3111', '1122', '0113', '1311', '3213', '1111', '0103', '0220', '1313', '0301', '1101', '0011', '1003', '3033', '1121', '0111', '1331', '3101', '1122', '0131', '1113', '1112', '3030', '1110']\n"
     ]
    }
   ],
   "source": [
    "tensor=[]\n",
    "reads=[]\n",
    "sig=[]\n",
    "for x in range(10):\n",
    "    with open('concat_{}.csv'.format(x)) as csvfile:\n",
    "        concat = csv.reader(csvfile, delimiter=' ')\n",
    "        for row in concat:\n",
    "            r=(''.join(row))\n",
    "            tensor.append(r)\n",
    "for x in range(len(tensor)):\n",
    "#print(tensor[0])\n",
    "    b=(tensor[x][:100:25])\n",
    "    reads.append(b)\n",
    "    c=(tensor[x][100:])\n",
    "    #print(c)\n",
    "    sig.append(c)\n",
    "print(reads) # this changes the array to a set and removes all the duplicate calls\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training entries: 8983, labels: 8983\n"
     ]
    }
   ],
   "source": [
    "print(\"Training entries: {}, labels: {}\".format(len(tensor), len(tensor)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Reader:\n",
    "    num_of_reads=0\n",
    "    def __init__(self, first, second, third, fourth):\n",
    "        self.first = first\n",
    "        self.second = second\n",
    "        self.third = third\n",
    "        self.fourth = fourth\n",
    "        self.fourmer = first + second + third + fourth\n",
    "        \n",
    "        Reader.num_of_reads+=1 # only do this where there is no case when you want to change a single instance\n",
    "        \n",
    "    def fourmerstring(self):\n",
    "        return '{}{}{}{}'.format(self.first, self.second, self.third, self.fourth)\n",
    "\n",
    "ID=[]\n",
    "for x in range (10):\n",
    "    for y in range(len(reads[x])):      \n",
    "        a = Reader(reads[y][0], reads[y][1],reads[y][2], reads[y][3])\n",
    "        ID.append(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Employee:\n",
    "    num_of_emps = 0\n",
    "    raise_amount = 1.04\n",
    "    def __init__(self, first, last, pay):\n",
    "        self.first = first\n",
    "        self.last = last\n",
    "        self.pay = pay\n",
    "        self.email = first +last+ '@company.com'\n",
    "        \n",
    "        Employee.num_of_emps+=1 # only do this where there is no case when you want to change a single instance\n",
    "        \n",
    "    def fullname(self):\n",
    "        return '{} {}'.format(self.first, self.last)\n",
    "    \n",
    "    def apply_raise(self):\n",
    "            self.pay = int(self.pay * self.raise_amount)\n",
    "    \n",
    "emp_1 = Employee('Corey', 'Schafer', 5000)\n",
    "emp_2 = Employee ('Michael', 'Jochum', 6000)\n",
    "\n",
    "Employee.fullname(emp_1)\n",
    "emp_1.apply_raise()\n",
    "#Employee.raise_amount = 1.05 #This changes the raise amount to the class and all the instances!\n",
    "emp_1.raise_amount = 1.05 \n",
    "# this returns a new raise amount for a single instance one only (instead of the classes raise amount)\n",
    "print(Employee.raise_amount)\n",
    "print(emp_1.raise_amount)\n",
    "print(emp_2.raise_amount)\n",
    "print(emp_1.__dict__) #This will print out everything possible attribute we can call from the class\n",
    "print(Employee.num_of_emps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_8 (Embedding)      (None, None, 16)          160000    \n",
      "_________________________________________________________________\n",
      "global_average_pooling1d_8 ( (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 160,289\n",
      "Trainable params: 160,289\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# input shape is the vocabulary count used for the movie reviews (10,000 words)\n",
    "vocab_size = 10000\n",
    "\n",
    "model = keras.Sequential()\n",
    "model.add(keras.layers.Embedding(vocab_size, 16))\n",
    "model.add(keras.layers.GlobalAveragePooling1D())\n",
    "model.add(keras.layers.Dense(16, activation=tf.nn.relu))\n",
    "model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))\n",
    "\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.train.AdamOptimizer(),\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: '9.273928895068479505e-01,9.273928895068479505e-01,9.273928895068479505e-01,9.273928895068479505e-01,8.729275921059026500e-01,8.729275921059026500e-01,8.729275921059026500e-01,8.729275921059026500e-01",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-515-ca7fa610d60e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx_val\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#I'm trying to convert my reads and sig to tensor here and its not working\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mpartial_x_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0my_val\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreads\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mpartial_y_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreads\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: '9.273928895068479505e-01,9.273928895068479505e-01,9.273928895068479505e-01,9.273928895068479505e-01,8.729275921059026500e-01,8.729275921059026500e-01,8.729275921059026500e-01,8.729275921059026500e-01"
     ]
    }
   ],
   "source": [
    "x_val =np.int64((sig[:10])) #I'm trying to convert my reads and sig to tensor here and its not working\n",
    "partial_x_train = list(sig[10:])\n",
    "\n",
    "y_val =list(reads[:10])\n",
    "partial_y_train = list(reads[10:])\n",
    "print(x_val[0])\n",
    "\n",
    "array_inspect(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'ndim'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-516-8c20653ab8cc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m                     \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m512\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m                     \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m                     verbose=1)\n\u001b[0m",
      "\u001b[1;32mc:\\users\\algae\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1507\u001b[0m         \u001b[0msteps_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'steps_per_epoch'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1508\u001b[0m         \u001b[0msteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1509\u001b[1;33m         validation_split=validation_split)\n\u001b[0m\u001b[0;32m   1510\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1511\u001b[0m     \u001b[1;31m# Prepare validation data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\algae\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split)\u001b[0m\n\u001b[0;32m    991\u001b[0m       \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext_element\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    992\u001b[0m     x, y, sample_weights = self._standardize_weights(x, y, sample_weight,\n\u001b[1;32m--> 993\u001b[1;33m                                                      class_weight, batch_size)\n\u001b[0m\u001b[0;32m    994\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    995\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\algae\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_weights\u001b[1;34m(self, x, y, sample_weight, class_weight, batch_size)\u001b[0m\n\u001b[0;32m   1110\u001b[0m         \u001b[0mfeed_input_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1111\u001b[0m         \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# Don't enforce the batch size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1112\u001b[1;33m         exception_prefix='input')\n\u001b[0m\u001b[0;32m   1113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1114\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\algae\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m    275\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'DataFrame'\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    276\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 277\u001b[1;33m   \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mstandardize_single_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    278\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    279\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\algae\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    275\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'DataFrame'\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    276\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 277\u001b[1;33m   \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mstandardize_single_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    278\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    279\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\algae\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_single_array\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    213\u001b[0m   \u001b[1;32melif\u001b[0m \u001b[0mtensor_util\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 215\u001b[1;33m   \u001b[1;32melif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    216\u001b[0m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    217\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'str' object has no attribute 'ndim'"
     ]
    }
   ],
   "source": [
    "history = model.fit(partial_x_train,\n",
    "                    partial_y_train,\n",
    "                    epochs=40,\n",
    "                    batch_size=512,\n",
    "                    validation_data=(x_val, y_val),\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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