dbn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import msgpack\n",
    "import glob\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.ops import control_flow_ops\n",
    "from tqdm import tqdm\n",
    "import h5py\n",
    "I=0\n",
    "def da(batch_size, layer_num, weights, biases):\n",
    "    global I    \n",
    "    for I in range(0,7000,batch_size):\n",
    "        h5f = h5py.File('reframed_input.h5','r')\n",
    "        Data = h5f['reframed'][I:I+batch_size]\n",
    "        h5f.close()\n",
    "        for j in range(layer_num):\n",
    "            #print(weights[j].shape)\n",
    "            #print(biases[j].shape)\n",
    "            Data = np.matmul(Data,weights[j])+biases[j]\n",
    "        yield(Data)\n",
    "def rbm(n_visible, n_hidden, num_epochs, batch_size, lr, ws, bs, layer_n):\n",
    "    ### HyperParameters\n",
    "    # First, let's take a look at the hyperparameters of our model:\n",
    "\n",
    "#     n_visible      = 1799 #This is the size of the visible layer. \n",
    "#     n_hidden       = 50 #This is the size of the hidden layer\n",
    "\n",
    "#     num_epochs = 20 #The number of training epochs that we are going to run. For each epoch we go through the entire data set.\n",
    "#     batch_size = 128 #The number of training examples that we are going to send through the RBM at a time. \n",
    "#     lr         = tf.constant(0.0005, tf.float32) #The learning rate of our model\n",
    "\n",
    "    ### Variables:\n",
    "    # Next, let's look at the variables we're going to use:\n",
    "\n",
    "    x  = tf.placeholder(tf.float32, [None, n_visible], name=\"x\") #The placeholder variable that holds our data\n",
    "    W  = tf.Variable(tf.random_normal([n_visible, n_hidden], 0.01), name=\"W\") #The weight matrix that stores the edge weights\n",
    "    bh = tf.Variable(tf.zeros([1, n_hidden],  tf.float32, name=\"bh\")) #The bias vector for the hidden layer\n",
    "    bv = tf.Variable(tf.zeros([1, n_visible],  tf.float32, name=\"bv\")) #The bias vector for the visible layer\n",
    "\n",
    "\n",
    "    #### Helper functions. \n",
    "\n",
    "    #This function lets us easily sample from a vector of probabilities\n",
    "    def sample(probs):\n",
    "        #Takes in a vector of probabilities, and returns a random vector of 0s and 1s sampled from the input vector\n",
    "        return tf.floor(probs + tf.random_uniform(tf.shape(probs), 0, 1))\n",
    "\n",
    "\n",
    "    #This function runs the gibbs chain. We will call this function in two places:\n",
    "    #    - When we define the training update step\n",
    "    #    - When we sample our music segments from the trained RBM\n",
    "    def gibbs_sample(k):\n",
    "        #Runs a k-step gibbs chain to sample from the probability distribution of the RBM defined by W, bh, bv\n",
    "        def gibbs_step(count, k, xk):\n",
    "            #Runs a single gibbs step. The visible values are initialized to xk\n",
    "            hk = sample(tf.sigmoid(tf.matmul(xk, W) + bh)) #Propagate the visible values to sample the hidden values\n",
    "            #xk = sample(tf.sigmoid(tf.matmul(hk, tf.transpose(W)) + bv)) #Propagate the hidden values to sample the visible values\n",
    "            #hk = sample(tf.matmul(xk, W) + bh)\n",
    "            #xk = sample(tf.matmul(hk, tf.transpose(W)) + bv)\n",
    "            #hk = sample(tf.sigmoid(tf.matmul(xk, W) + bh))\n",
    "            xk = sample(tf.truncated_normal((1,n_visible),tf.matmul(hk, tf.transpose(W)) + bv,1))\n",
    "            return count+1, k, xk\n",
    "\n",
    "        #Run gibbs steps for k iterations\n",
    "        ct = tf.constant(0) #counter\n",
    "        [_, _, x_sample] = control_flow_ops.while_loop(lambda count, num_iter, *args: count < num_iter,\n",
    "                                             gibbs_step, [ct, tf.constant(k), x], back_prop = False)\n",
    "        #This is not strictly necessary in this implementation, but if you want to adapt this code to use one of TensorFlow's\n",
    "        #optimizers, you need this in order to stop tensorflow from propagating gradients back through the gibbs step\n",
    "        x_sample = tf.stop_gradient(x_sample) \n",
    "        return x_sample\n",
    "\n",
    "\n",
    "    ### Training Update Code\n",
    "    # Now we implement the contrastive divergence algorithm. First, we get the samples of x and h from the probability distribution\n",
    "    #The sample of x\n",
    "    x_sample = gibbs_sample(1) \n",
    "    #The sample of the hidden nodes, starting from the visible state of x\n",
    "    h = sample(tf.sigmoid(tf.matmul(x, W) + bh)) \n",
    "    #The sample of the hidden nodes, starting from the visible state of x_sample\n",
    "    h_sample = sample(tf.sigmoid(tf.matmul(x_sample, W) + bh)) \n",
    "\n",
    "    #Next, we update the values of W, bh, and bv, based on the difference between the samples that we drew and the original values\n",
    "    size_bt = tf.cast(tf.shape(x)[0], tf.float32)\n",
    "    W_adder  = tf.multiply(lr/size_bt, tf.subtract(tf.matmul(tf.transpose(x), h), tf.matmul(tf.transpose(x_sample), h_sample)))\n",
    "    bv_adder = tf.multiply(lr/size_bt, tf.reduce_sum(tf.subtract(x, x_sample), 0, True))\n",
    "    bh_adder = tf.multiply(lr/size_bt, tf.reduce_sum(tf.subtract(h, h_sample), 0, True))\n",
    "    #When we do sess.run(updt), TensorFlow will run all 3 update steps\n",
    "    updt = [W.assign_add(W_adder), bv.assign_add(bv_adder), bh.assign_add(bh_adder)]\n",
    "\n",
    "\n",
    "\n",
    "    ### Run the graph!\n",
    "    # Now it's time to start a session and run the graph! \n",
    "\n",
    "    with tf.Session() as sess:\n",
    "        #First, we train the model\n",
    "        #initialize the variables of the model\n",
    "        init = tf.initialize_all_variables()\n",
    "        sess.run(init)\n",
    "        #Run through all of the training data num_epochs times\n",
    "        for epoch in tqdm(range(num_epochs)):\n",
    "            #Train the RBM on batch_size examples at a time\n",
    "            for X_batch in da(batch_size, layer_n, ws, bs):\n",
    "                feed_dict = {x: X_batch} \n",
    "                var1 = sess.run(updt, feed_dict)\n",
    "    return var1\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py:118: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                            | 0/1 [00:00<?, ?it/s]"
     ]
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      "(1799, 1799)\n",
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     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.39s/it]\n"
     ]
    }
   ],
   "source": [
    "visible = 1799\n",
    "hidden = 1000\n",
    "visible2 = 50\n",
    "hidden2 = 30\n",
    "visible2 = 30\n",
    "hidden2 = 10\n",
    "layer1 = rbm(visible, hidden, 1, 128, 0.0005, [np.eye(visible)], [np.zeros((1,visible))],1)\n",
    "#layer2 = rbm(visible2, hidden2, 1, 128, 0.0005, [np.eye(visible),layer1[0]], [np.zeros((1,visible)),layer1[2]],2)\n",
    "#layer2 = rbm(visible2, hidden2, 1, 128, 0.0005, [np.eye(visible),layer1[0],layer2[0]], [np.zeros((1,visible)),layer1[2],layer2[2]],2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "for X_batch in da(batch_size):\n",
    "    np.matmul(X_batch,layer1[0])+layer1[2] \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for X in Data_test:\n",
    "    np.matmul(X,layer1[0])+layer1[2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1799, 1000)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer1[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "h5f = h5py.File('reframed_input.h5','r')\n",
    "Data_test = h5f['reframed'][0:100]\n",
    "h5f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|                                                                                           | 0/20 [00:00<?, ?it/s]"
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     "text": [
      "0\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n"
     ]
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    {
     "name": "stderr",
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     "text": [
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Cannot feed value of shape (128, 50) for Tensor 'x:0', which has shape '(?, 1799)'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-65-21219143b1ff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     12\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m             \u001b[0mfeed_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_batch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m             \u001b[0mvar2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mupdt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    898\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    899\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 900\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    901\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    902\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1109\u001b[0m                              \u001b[1;34m'which has shape %r'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1110\u001b[0m                              (np_val.shape, subfeed_t.name,\n\u001b[1;32m-> 1111\u001b[1;33m                               str(subfeed_t.get_shape())))\n\u001b[0m\u001b[0;32m   1112\u001b[0m           \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_feedable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubfeed_t\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Tensor %s may not be fed.'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0msubfeed_t\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Cannot feed value of shape (128, 50) for Tensor 'x:0', which has shape '(?, 1799)'"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    #First, we train the model\n",
    "    #initialize the variables of the model\n",
    "    init = tf.initialize_all_variables()\n",
    "    sess.run(init)\n",
    "    #Run through all of the training data num_epochs times\n",
    "    for epoch in tqdm(range(num_epochs)):\n",
    "        #Train the RBM on batch_size examples at a time\n",
    "        for X_batch in da(batch_size):\n",
    "            print(X_batch.astype(np.float32).shape)\n",
    "            print(var[0].shape)\n",
    "            print(var[2].shape)\n",
    "            feed_dict = {x: np.matmul(X_batch.astype(np.float32),var[0])+var[2]} \n",
    "            var2 = sess.run(updt, feed_dict)"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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      "384\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "512\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "640\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "768\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "896\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "1024\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "1152\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "1280\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "1408\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "1536\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "1664\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "1792\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "1920\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "2048\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "2176\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "2304\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "2432\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "2560\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "2688\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "2816\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "2944\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "3072\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "3200\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "3328\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "3456\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "3584\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "3712\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "3840\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "3968\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "4096\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "4224\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "4352\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "4480\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "4608\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "4736\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "4864\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "4992\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "5120\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "5248\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "5376\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "5504\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "5632\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "5760\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "5888\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "6016\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "6144\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "6272\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "6400\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "6528\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "6656\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "6784\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n",
      "6912\n",
      "(128, 1799)\n",
      "(1799, 50)\n",
      "(1, 50)\n",
      "(128, 50)\n"
     ]
    }
   ],
   "source": [
    "for X_batch in da(batch_size):\n",
    "    print(X_batch.shape)\n",
    "    print(var[0].shape)\n",
    "    print(var[2].shape)\n",
    "    print((np.matmul(X_batch.astype(np.float32),var[0])+var[2]).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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