QNN_data_reuploading_Sreetama.ipynb

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   "source": [
    "from jax import numpy as jnp\n",
    "import jax\n",
    "import optax\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, ConfusionMatrixDisplay, confusion_matrix, f1_score\n",
    "from quantum_model import quantum_neural_network\n",
    "import pennylane as qml\n",
    "from sklearn.utils import gen_batches\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Introduction:\n",
    "## Good morning, \n",
    "This is a simple tutorial on quantum neural network (QNN) with **data-reuploding techinque**. The neural net is trained on the MNIST dataset initially (the classification is binary, we select 0,1 classes), and then, we try to evaluate it also on a specific case of MedMNIST dataset, trying to be more realistic. Lastly, we implement a QCNN and we will see what happens with it.\n",
    "I recommend that I realized this work by combining some parts taken from other repos and adapted to my problem. I have some little doubts on few parts, but I will try to explain everything as I can.\n",
    "\n",
    "**Requirements:**\n",
    "\n",
    "**JAX** (for optimization, a specific version of numpy which suits with JAX functions and vectorization, but PyTorch is good anyway);\n",
    "\n",
    "**optax** (for optimizers to combine with JAX);\n",
    "\n",
    "**tensorflow and Keras** (to create the dataset and to use the *resize* method to downsample an image);\n",
    "\n",
    "**sklearn** (for accuracy_score in the test, gen_batches to generate batches of images and labels and to split train, val, test set);\n",
    "\n",
    "**Pennylane** (for quantum computing).\n"
   ],
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   "source": [
    "## Dataset\n",
    "\n",
    "We define the **downsample** function to resize or downsample an image in terms of pixels. Obviosouly, I will not use (28x28) MNIST images on a quantum nn since the number of parameters inside rotational gates would increase too much. Rather, I will work with less pixels/features.\n",
    "**RandomMnist** is a class that create only train and validation sets from the training MNIST images inside Keras. Note that the images only come from the train tuple of Keras and we use sklearn to extract a fraction of them to create the smaller validation set. By doing so, we are sure that the images we would use in the TEST set wouldn't be contained in the previous train and val sets. \n",
    "Inside the class we declare the number of training images to work with, the specific classes, a shuffle to shuffle the dataset before extracting the vectors of Xs and ys, a resize integer to resize the image according to the vlaue we insert and a seed for reproducibility, i. e. using the same seed the train and validation set will be the same even though we apply the same method many times.\n"
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   "source": [
    "def down_sample(x_array: np.ndarray, size: int) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Function that reduces size of the images array.\n",
    "    Args:\n",
    "        x_array (array): image array.\n",
    "        size (int): new size to resize.\n",
    "    Returns:\n",
    "        new_array (array): resized image.\n",
    "    \"\"\"\n",
    "    new_size = (size, size)\n",
    "    x_array = np.reshape(x_array, (x_array.shape[0], x_array.shape[1], 1))\n",
    "    new_array = tf.image.resize(x_array, new_size)\n",
    "    return new_array.numpy()\n",
    "\n",
    "class RandomMnist:\n",
    "    \"\"\"\n",
    "    Class that creates a binary dataset out of Mnist after specifying the classes of images to deal with,\n",
    "    based on Keras's API.\n",
    "    The dataset created here is composed by train and validation set.\n",
    "    Args:\n",
    "        classes_of_items (list): classes of images;\n",
    "        num_train_samples (int): number of training images;\n",
    "        shuffle (bool): if True the images are shuffled before creating the dataset;\n",
    "        resize (int): new image dimension (it must be lower than 28);\n",
    "        my_seed (int): seed for reproducibility.\n",
    "    Returns:\n",
    "        X_train (array): set of training images of shape (num_images, new_shape*new_shape);\n",
    "        y_train (array): set of training labels of shape (num_images,);\n",
    "        X_val (array): set of validation images of shape (num_images, new_shape*new_shape);\n",
    "        y_val (array): set of validation labels of shape (num_images,).\n",
    "    Note that the number of validation data is 0.2*num_train_samples.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, classes_of_items: list, num_train_samples: int, shuffle: bool,\n",
    "                 resize: int, my_seed: int) -> None:\n",
    "        self.classes_of_items = classes_of_items\n",
    "        self.num_train_samples = num_train_samples\n",
    "        self.shuffle = shuffle\n",
    "        self.resize = resize\n",
    "        self.my_seed = my_seed\n",
    "\n",
    "    def data(self) -> tuple:\n",
    "        np.random.seed(self.my_seed)\n",
    "        (train_X, train_y), (_, _) = mnist.load_data()\n",
    "        X_train_filtered = train_X[np.isin(train_y, [self.classes_of_items[0], self.classes_of_items[1]])]\n",
    "        y_train_filtered = train_y[np.isin(train_y, [self.classes_of_items[0], self.classes_of_items[1]])]\n",
    "\n",
    "        X_train_filtered = X_train_filtered.astype('float16') / 255\n",
    "        X_train_new = []\n",
    "        if self.resize is not None and self.resize <= 28:\n",
    "            for train in X_train_filtered:\n",
    "                X_train_new.append(down_sample(train, self.resize))\n",
    "        else:\n",
    "            raise Exception(\"The new size must be smaller than the actual Mnist size that is 28!\")\n",
    "        # shuffle\n",
    "        X_train_new = np.array(X_train_new)\n",
    "        if self.shuffle:\n",
    "            shuffled_indices = np.arange(len(X_train_new))\n",
    "            np.random.shuffle(shuffled_indices)\n",
    "            X_train_new = X_train_new[shuffled_indices]\n",
    "            y_train_filtered = y_train_filtered[shuffled_indices]\n",
    "\n",
    "        if self.num_train_samples is not None:\n",
    "            num_samples_per_class = self.num_train_samples // len(self.classes_of_items)\n",
    "            selected_indices = []\n",
    "            for class_idx in self.classes_of_items:\n",
    "                class_indices = np.where(y_train_filtered == class_idx)[0][:num_samples_per_class]\n",
    "                selected_indices.extend(class_indices)\n",
    "            X_train_ = X_train_new[selected_indices]\n",
    "            y_train_filtered = y_train_filtered[selected_indices]\n",
    "        else:\n",
    "            raise RuntimeError(\"Insert number of images to form the train set.\")\n",
    "\n",
    "        X_train_ = X_train_.reshape(X_train_.shape[0], X_train_.shape[1] * X_train_.shape[2])\n",
    "        X_train_final, X_val, y_train_final, y_val = train_test_split(\n",
    "            X_train_, y_train_filtered, test_size=0.2, random_state=42)\n",
    "\n",
    "        return (\n",
    "            jnp.asarray(X_train_final),\n",
    "            jnp.asarray(y_train_final),\n",
    "            jnp.asarray(X_val),\n",
    "            jnp.asarray(y_val),\n",
    "        )"
   ],
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   "id": "6a9b7d7bf38ae01c",
   "execution_count": 10
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   "cell_type": "markdown",
   "source": [
    "Note that the X_train, y_train, X_val and y_val are JAX arrays!"
   ],
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   "id": "b683d8f06444a758"
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   "cell_type": "markdown",
   "source": [
    "## Quantum model\n",
    "\n",
    "Let's create the quantum model with my method, but I will be happy to learn a new method!\n",
    "The class contains a feature map circuit which is composed by a sequence of U3 gates to embed all the 64 features (we downscale the images to be (8x8) MNIST images) and a parameterized ansatz to learn 69 parameters each layer. Now we will display everything and we will understand how they work."
   ],
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   "source": [
    "class quantum_neural_network:\n",
    "    def __init__(self, n_wires: int) -> None:\n",
    "        self.n_wires = n_wires\n",
    "    def QuantumMap(self, X_data: jnp.ndarray) -> None:\n",
    "        \"\"\"\n",
    "            Quantum feature map composed by U3 gates.\n",
    "            :arg:\n",
    "                X_data: jax.numpy array input image.\n",
    "            :return:\n",
    "                quantum feature map to map classical features into quantum state.\n",
    "        \"\"\"\n",
    "        idx = 0\n",
    "        qml.Barrier(only_visual=True)\n",
    "        for i in range(self.n_wires):\n",
    "            qml.Rot(phi=X_data[idx + 0], theta=X_data[idx + 1], omega=X_data[idx + 2], wires=i)\n",
    "            idx += 3\n",
    "            qml.Rot(phi=X_data[idx + 0], theta=X_data[idx + 1], omega=X_data[idx + 2], wires=i)\n",
    "            idx += 3\n",
    "        for j in range(5):\n",
    "            qml.Rot(phi=X_data[idx + 0], theta=X_data[idx + 1], omega=X_data[idx + 2], wires=j)\n",
    "            idx += 3\n",
    "        qml.RZ(phi=X_data[63], wires=5)  # Only 64 features\n",
    "        qml.Barrier(only_visual=True)\n",
    "    def QuantumRing_layer(self, params: jnp.ndarray) -> None:\n",
    "        \"\"\"\n",
    "            Quantum ansatz composed by U3 gates and ring-like CNOT entangling scheme.\n",
    "            :arg: self, params (jaa.numpy array of parameters).\n",
    "            :return: to be connected to the feature map.\n",
    "        \"\"\"\n",
    "        idx = 0\n",
    "        for i in range(self.n_wires):\n",
    "            qml.Rot(phi=params[0 + idx], theta=params[1 + idx], omega=params[2 + idx], wires=i)\n",
    "            idx += 3\n",
    "        qml.Barrier(only_visual=True)\n",
    "        for j in range(self.n_wires - 1):\n",
    "            qml.CRot(phi=params[idx + 0], theta=params[idx + 1], omega=params[idx + 2], wires=[j, j + 1])\n",
    "            idx += 3\n",
    "        qml.Barrier(only_visual=True)\n",
    "        for k in range(self.n_wires):\n",
    "            qml.Rot(phi=params[0 + idx], theta=params[1 + idx], omega=params[2 + idx], wires=k)\n",
    "            idx += 3"
   ],
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    "layers1 = 2  # Declare how many layers you want\n",
    "n_qubits = 8\n",
    "dev = qml.device('default.qubit', wires=n_qubits)\n",
    "\n",
    "@qml.qnode(dev, interface='jax')\n",
    "def quantum_nn(X, thetas):\n",
    "    model = quantum_neural_network(n_wires=n_qubits)\n",
    "    params_per_sublayer = 69  ### each layer will learn 69 parameters\n",
    "    for layer in range(layers1):\n",
    "        model.QuantumMap(X)\n",
    "        j = layer * params_per_sublayer\n",
    "        model.QuantumRing_layer(thetas[j:j+(params_per_sublayer)],)\n",
    "        j += (params_per_sublayer)*(layer)\n",
    "    qml.Barrier(wires=range(n_qubits),only_visual=True)\n",
    "    return qml.probs(wires=0)"
   ],
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   "source": [
    "We work with 8 **qubits**.\n",
    "Since we use always U3 gates we have 3 **param_per_gate**. \n",
    "We use also ControlledU3 gates for entanglement so we have one gate less with respect to the number of qubits -> **entangling_gate = qubits - 1**."
   ],
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   "source": [
    "param_per_gate = 3 \n",
    "entangling_gate = 7\n",
    "new_shape = 8\n",
    "# Call the train and val sets\n",
    "dataset = RandomMnist(classes_of_items=[0, 1], num_train_samples=500, shuffle=True, resize=new_shape, my_seed=999)\n",
    "X_train, y_train, X_val, y_val = dataset.data()"
   ],
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     "data": {
      "text/plain": "(<Figure size 3600x900 with 1 Axes>, <Axes: >)"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seed = 78\n",
    "key = jax.random.PRNGKey(seed)\n",
    "initial_params = jax.random.normal(key, shape=((param_per_gate * n_qubits + param_per_gate * entangling_gate +\n",
    "                                                param_per_gate * n_qubits) * layers1,))\n",
    "qml.draw_mpl(qnode=quantum_nn, style=\"sketch\", decimals=1)(X_train[0], initial_params)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-16T10:03:19.891537Z",
     "start_time": "2024-03-16T10:03:17.907181Z"
    }
   },
   "id": "c7b614f1e04c63a9",
   "execution_count": 19
  },
  {
   "cell_type": "markdown",
   "source": [
    "As we can see, the parameters inside the ansatz are different in each layer, while features remain the same and are re-uploaded at each layer, whose number could be modified. The probabilities of getting a class 0 or 1 are measured after the circuit. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6ee12a6e2e196ecf"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Useful functions before starting\n",
    "\n",
    "These functions are:\n",
    " \n",
    "**binary_crossentropy** which computes the output (i.e., a probability of getting the image 0 or 1) and loops through each output (which is an array of the form (number_of_elements, 2) since for each element I have 2 probabilities) and each true label (which is a vector of\n",
    "(number_of_elements, 1)) and applies binary cross-entropy formula;\n",
    "\n",
    "**optimizer_update** which updates the parameters after each iteration;\n",
    "\n",
    "**accuracy** which calculates the output, extracts the index of the largest probability and compares it to the corresponding true label.\n",
    "\n",
    "The **@jax.jit** should need for optimization.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8eed644cac20bf42"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "@jax.jit\n",
    "def binary_crossentropy(x, y, theta):\n",
    "    labels = jnp.array(y)\n",
    "    pred = jnp.array(qnn(x, theta))\n",
    "    loss = 0\n",
    "    for l, p in zip(labels, pred):\n",
    "        loss += l * (jnp.log(p[l])) + (1 - l) * jnp.log(1 - p[1 - l])\n",
    "    return -jnp.sum(loss)/len(labels)\n",
    "\n",
    "@jax.jit\n",
    "def optimizer_update(opt_state, params, x, y):\n",
    "    loss_value, grads = jax.value_and_grad(lambda theta: binary_crossentropy(x, y, theta))(params)\n",
    "    updates, opt_state = optimizer.update(grads, opt_state)\n",
    "    params = optax.apply_updates(params, updates)\n",
    "    return params, opt_state, loss_value\n",
    "\n",
    "@jax.jit\n",
    "def accuracy(X, y, params):\n",
    "    labels = jnp.array(y)\n",
    "    predictions = jnp.array(qnn(X, params))\n",
    "    correct_predictions = jnp.sum(jnp.argmax(predictions, axis=1) == labels)\n",
    "    return correct_predictions / len(labels)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-16T14:05:38.016417Z",
     "start_time": "2024-03-16T14:05:37.947262Z"
    }
   },
   "id": "193f1d31aac10bc9",
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Set some hyper-parameters"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "579ed285c1d36d63"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "lr = 0.01\n",
    "n_epochs = 200\n",
    "batch_size = 32\n",
    "\n",
    "qnn_batched = jax.vmap(quantum_nn, (0, None,)) # use vmap to apply quantum_nn function to a vector, it's typical in JAX.\n",
    "qnn = jax.jit(qnn_batched)\n",
    "optimizer = optax.adam(learning_rate=lr)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-16T14:07:48.399438Z",
     "start_time": "2024-03-16T14:07:48.377143Z"
    }
   },
   "id": "74cd97deb01424cd",
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, ---Train loss:  0.6017588 ---Val loss:  0.63504195 ---Train acc:  0.715 ---Val acc.:  0.59999996\n",
      "Epoch: 2, ---Train loss:  0.5841071 ---Val loss:  0.6424547 ---Train acc:  0.78749996 ---Val acc.:  0.72999996\n",
      "Epoch: 3, ---Train loss:  0.55228055 ---Val loss:  0.5927823 ---Train acc:  0.79249996 ---Val acc.:  0.69\n",
      "Epoch: 4, ---Train loss:  0.5312481 ---Val loss:  0.5722231 ---Train acc:  0.805 ---Val acc.:  0.74\n",
      "Epoch: 5, ---Train loss:  0.51533693 ---Val loss:  0.5578808 ---Train acc:  0.8275 ---Val acc.:  0.76\n",
      "Epoch: 6, ---Train loss:  0.50208616 ---Val loss:  0.5469006 ---Train acc:  0.8325 ---Val acc.:  0.74\n",
      "Epoch: 7, ---Train loss:  0.49140972 ---Val loss:  0.5306903 ---Train acc:  0.825 ---Val acc.:  0.75\n",
      "Epoch: 8, ---Train loss:  0.4814161 ---Val loss:  0.52094 ---Train acc:  0.835 ---Val acc.:  0.74\n",
      "Epoch: 9, ---Train loss:  0.47307375 ---Val loss:  0.5180834 ---Train acc:  0.8325 ---Val acc.:  0.74\n",
      "Epoch: 10, ---Train loss:  0.46488482 ---Val loss:  0.506855 ---Train acc:  0.8325 ---Val acc.:  0.75\n",
      "Epoch: 11, ---Train loss:  0.4575598 ---Val loss:  0.50134224 ---Train acc:  0.8275 ---Val acc.:  0.75\n",
      "Epoch: 12, ---Train loss:  0.45064142 ---Val loss:  0.49567142 ---Train acc:  0.83 ---Val acc.:  0.75\n",
      "Epoch: 13, ---Train loss:  0.4443649 ---Val loss:  0.49224952 ---Train acc:  0.8325 ---Val acc.:  0.75\n",
      "Epoch: 14, ---Train loss:  0.43826383 ---Val loss:  0.47968322 ---Train acc:  0.84999996 ---Val acc.:  0.78999996\n",
      "Epoch: 15, ---Train loss:  0.43228272 ---Val loss:  0.4790241 ---Train acc:  0.835 ---Val acc.:  0.76\n",
      "Epoch: 16, ---Train loss:  0.42705074 ---Val loss:  0.4699916 ---Train acc:  0.85249996 ---Val acc.:  0.79999995\n",
      "Epoch: 17, ---Train loss:  0.42262012 ---Val loss:  0.4705902 ---Train acc:  0.84749997 ---Val acc.:  0.78999996\n",
      "Epoch: 18, ---Train loss:  0.41812223 ---Val loss:  0.4614397 ---Train acc:  0.865 ---Val acc.:  0.79999995\n",
      "Epoch: 19, ---Train loss:  0.41579804 ---Val loss:  0.46248776 ---Train acc:  0.865 ---Val acc.:  0.78999996\n",
      "Epoch: 20, ---Train loss:  0.4110948 ---Val loss:  0.45558944 ---Train acc:  0.87 ---Val acc.:  0.83\n",
      "Epoch: 21, ---Train loss:  0.40833816 ---Val loss:  0.45423278 ---Train acc:  0.8725 ---Val acc.:  0.82\n",
      "Epoch: 22, ---Train loss:  0.40667048 ---Val loss:  0.45845416 ---Train acc:  0.875 ---Val acc.:  0.79999995\n",
      "Epoch: 23, ---Train loss:  0.4034051 ---Val loss:  0.4451164 ---Train acc:  0.8725 ---Val acc.:  0.84\n",
      "Epoch: 24, ---Train loss:  0.40184364 ---Val loss:  0.45272285 ---Train acc:  0.8875 ---Val acc.:  0.82\n",
      "Epoch: 25, ---Train loss:  0.39988884 ---Val loss:  0.44673225 ---Train acc:  0.875 ---Val acc.:  0.84\n",
      "Epoch: 26, ---Train loss:  0.39784685 ---Val loss:  0.44417468 ---Train acc:  0.8775 ---Val acc.:  0.84\n",
      "Epoch: 27, ---Train loss:  0.39664486 ---Val loss:  0.4421514 ---Train acc:  0.8775 ---Val acc.:  0.83\n",
      "Epoch: 28, ---Train loss:  0.39523685 ---Val loss:  0.440951 ---Train acc:  0.8775 ---Val acc.:  0.84\n",
      "Epoch: 29, ---Train loss:  0.39419138 ---Val loss:  0.43694457 ---Train acc:  0.875 ---Val acc.:  0.84999996\n",
      "Epoch: 30, ---Train loss:  0.3932642 ---Val loss:  0.43577453 ---Train acc:  0.875 ---Val acc.:  0.84999996\n",
      "Epoch: 31, ---Train loss:  0.39237678 ---Val loss:  0.44038132 ---Train acc:  0.8775 ---Val acc.:  0.83\n",
      "Epoch: 32, ---Train loss:  0.39305937 ---Val loss:  0.4446952 ---Train acc:  0.8825 ---Val acc.:  0.83\n",
      "Epoch: 33, ---Train loss:  0.3918816 ---Val loss:  0.42995688 ---Train acc:  0.8775 ---Val acc.:  0.87\n",
      "Epoch: 34, ---Train loss:  0.39098868 ---Val loss:  0.43866256 ---Train acc:  0.885 ---Val acc.:  0.83\n",
      "Epoch: 35, ---Train loss:  0.3913626 ---Val loss:  0.4313582 ---Train acc:  0.88 ---Val acc.:  0.87\n",
      "Epoch: 36, ---Train loss:  0.38991904 ---Val loss:  0.4390602 ---Train acc:  0.885 ---Val acc.:  0.83\n",
      "Epoch: 37, ---Train loss:  0.38885581 ---Val loss:  0.43354225 ---Train acc:  0.8775 ---Val acc.:  0.84999996\n",
      "Epoch: 38, ---Train loss:  0.3885565 ---Val loss:  0.4305326 ---Train acc:  0.8775 ---Val acc.:  0.85999995\n",
      "Epoch: 39, ---Train loss:  0.38870183 ---Val loss:  0.43836445 ---Train acc:  0.885 ---Val acc.:  0.83\n",
      "Epoch: 40, ---Train loss:  0.38783517 ---Val loss:  0.43307385 ---Train acc:  0.8775 ---Val acc.:  0.85999995\n",
      "Epoch: 41, ---Train loss:  0.38923466 ---Val loss:  0.43438116 ---Train acc:  0.8875 ---Val acc.:  0.84\n",
      "Epoch: 42, ---Train loss:  0.38802138 ---Val loss:  0.43509462 ---Train acc:  0.8775 ---Val acc.:  0.85999995\n",
      "Epoch: 43, ---Train loss:  0.3874116 ---Val loss:  0.42826357 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 44, ---Train loss:  0.38715547 ---Val loss:  0.4354457 ---Train acc:  0.8775 ---Val acc.:  0.84\n",
      "Epoch: 45, ---Train loss:  0.3865597 ---Val loss:  0.43003035 ---Train acc:  0.88 ---Val acc.:  0.85999995\n",
      "Epoch: 46, ---Train loss:  0.3865434 ---Val loss:  0.43316317 ---Train acc:  0.88 ---Val acc.:  0.84\n",
      "Epoch: 47, ---Train loss:  0.38630012 ---Val loss:  0.4289724 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 48, ---Train loss:  0.38627896 ---Val loss:  0.43321642 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 49, ---Train loss:  0.3866001 ---Val loss:  0.42870852 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 50, ---Train loss:  0.38866946 ---Val loss:  0.44211337 ---Train acc:  0.89 ---Val acc.:  0.83\n",
      "Epoch: 51, ---Train loss:  0.3870693 ---Val loss:  0.42779005 ---Train acc:  0.88 ---Val acc.:  0.88\n",
      "Epoch: 52, ---Train loss:  0.38657722 ---Val loss:  0.43173534 ---Train acc:  0.885 ---Val acc.:  0.84999996\n",
      "Epoch: 53, ---Train loss:  0.3856451 ---Val loss:  0.42846024 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 54, ---Train loss:  0.3879396 ---Val loss:  0.43881267 ---Train acc:  0.89 ---Val acc.:  0.83\n",
      "Epoch: 55, ---Train loss:  0.38590968 ---Val loss:  0.42649773 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 56, ---Train loss:  0.38548648 ---Val loss:  0.42876032 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 57, ---Train loss:  0.38615012 ---Val loss:  0.43444172 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 58, ---Train loss:  0.38571256 ---Val loss:  0.43555233 ---Train acc:  0.885 ---Val acc.:  0.83\n",
      "Epoch: 59, ---Train loss:  0.3863212 ---Val loss:  0.43777725 ---Train acc:  0.89 ---Val acc.:  0.83\n",
      "Epoch: 60, ---Train loss:  0.38501704 ---Val loss:  0.43024635 ---Train acc:  0.88 ---Val acc.:  0.85999995\n",
      "Epoch: 61, ---Train loss:  0.38501346 ---Val loss:  0.42892224 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 62, ---Train loss:  0.3850433 ---Val loss:  0.4291183 ---Train acc:  0.88 ---Val acc.:  0.85999995\n",
      "Epoch: 63, ---Train loss:  0.3851244 ---Val loss:  0.4315457 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 64, ---Train loss:  0.38487202 ---Val loss:  0.42723697 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 65, ---Train loss:  0.38507742 ---Val loss:  0.4264984 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 66, ---Train loss:  0.3889632 ---Val loss:  0.44649193 ---Train acc:  0.895 ---Val acc.:  0.82\n",
      "Epoch: 67, ---Train loss:  0.3866174 ---Val loss:  0.4236363 ---Train acc:  0.88 ---Val acc.:  0.87\n",
      "Epoch: 68, ---Train loss:  0.38513878 ---Val loss:  0.43247828 ---Train acc:  0.8875 ---Val acc.:  0.84\n",
      "Epoch: 69, ---Train loss:  0.38493803 ---Val loss:  0.42597744 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 70, ---Train loss:  0.38527066 ---Val loss:  0.43455988 ---Train acc:  0.8875 ---Val acc.:  0.83\n",
      "Epoch: 71, ---Train loss:  0.3848898 ---Val loss:  0.42971504 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 72, ---Train loss:  0.3848259 ---Val loss:  0.42820275 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 73, ---Train loss:  0.38477218 ---Val loss:  0.42802802 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 74, ---Train loss:  0.38466364 ---Val loss:  0.43206486 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 75, ---Train loss:  0.3860586 ---Val loss:  0.4258449 ---Train acc:  0.88 ---Val acc.:  0.87\n",
      "Epoch: 76, ---Train loss:  0.38443 ---Val loss:  0.4285773 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 77, ---Train loss:  0.38519096 ---Val loss:  0.4239679 ---Train acc:  0.88 ---Val acc.:  0.87\n",
      "Epoch: 78, ---Train loss:  0.38432726 ---Val loss:  0.42812362 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 79, ---Train loss:  0.38441864 ---Val loss:  0.43165553 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 80, ---Train loss:  0.38461062 ---Val loss:  0.42914814 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 81, ---Train loss:  0.38411987 ---Val loss:  0.42903823 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 82, ---Train loss:  0.38471308 ---Val loss:  0.42550743 ---Train acc:  0.8875 ---Val acc.:  0.87\n",
      "Epoch: 83, ---Train loss:  0.38452837 ---Val loss:  0.4329078 ---Train acc:  0.8875 ---Val acc.:  0.84\n",
      "Epoch: 84, ---Train loss:  0.38450205 ---Val loss:  0.4328139 ---Train acc:  0.8825 ---Val acc.:  0.84999996\n",
      "Epoch: 85, ---Train loss:  0.3853478 ---Val loss:  0.4286221 ---Train acc:  0.89 ---Val acc.:  0.84999996\n",
      "Epoch: 86, ---Train loss:  0.38544807 ---Val loss:  0.43628192 ---Train acc:  0.895 ---Val acc.:  0.83\n",
      "Epoch: 87, ---Train loss:  0.3845497 ---Val loss:  0.43069038 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 88, ---Train loss:  0.3844479 ---Val loss:  0.4244772 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 89, ---Train loss:  0.38502583 ---Val loss:  0.43334192 ---Train acc:  0.8925 ---Val acc.:  0.83\n",
      "Epoch: 90, ---Train loss:  0.3849475 ---Val loss:  0.42606643 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 91, ---Train loss:  0.3840568 ---Val loss:  0.43189457 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 92, ---Train loss:  0.38386947 ---Val loss:  0.42764118 ---Train acc:  0.88 ---Val acc.:  0.85999995\n",
      "Epoch: 93, ---Train loss:  0.38388845 ---Val loss:  0.42801166 ---Train acc:  0.88 ---Val acc.:  0.85999995\n",
      "Epoch: 94, ---Train loss:  0.38416755 ---Val loss:  0.4311743 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 95, ---Train loss:  0.3846415 ---Val loss:  0.42881736 ---Train acc:  0.88 ---Val acc.:  0.87\n",
      "Epoch: 96, ---Train loss:  0.38434756 ---Val loss:  0.43342304 ---Train acc:  0.8875 ---Val acc.:  0.83\n",
      "Epoch: 97, ---Train loss:  0.38445777 ---Val loss:  0.42379147 ---Train acc:  0.88 ---Val acc.:  0.87\n",
      "Epoch: 98, ---Train loss:  0.384096 ---Val loss:  0.43077156 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 99, ---Train loss:  0.38390368 ---Val loss:  0.4283834 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 100, ---Train loss:  0.38411894 ---Val loss:  0.43329325 ---Train acc:  0.885 ---Val acc.:  0.84\n",
      "Epoch: 101, ---Train loss:  0.38450584 ---Val loss:  0.4256769 ---Train acc:  0.8875 ---Val acc.:  0.87\n",
      "Epoch: 102, ---Train loss:  0.38399765 ---Val loss:  0.42688632 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 103, ---Train loss:  0.38428062 ---Val loss:  0.4319761 ---Train acc:  0.89 ---Val acc.:  0.84\n",
      "Epoch: 104, ---Train loss:  0.38390753 ---Val loss:  0.42745543 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 105, ---Train loss:  0.3840517 ---Val loss:  0.42574236 ---Train acc:  0.885 ---Val acc.:  0.87\n",
      "Epoch: 106, ---Train loss:  0.38445082 ---Val loss:  0.43482405 ---Train acc:  0.89 ---Val acc.:  0.83\n",
      "Epoch: 107, ---Train loss:  0.38441542 ---Val loss:  0.42936474 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 108, ---Train loss:  0.38368142 ---Val loss:  0.42526317 ---Train acc:  0.885 ---Val acc.:  0.87\n",
      "Epoch: 109, ---Train loss:  0.38354915 ---Val loss:  0.42660052 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 110, ---Train loss:  0.3835484 ---Val loss:  0.42858404 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 111, ---Train loss:  0.38404518 ---Val loss:  0.42364183 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 112, ---Train loss:  0.3837659 ---Val loss:  0.4312342 ---Train acc:  0.8875 ---Val acc.:  0.84\n",
      "Epoch: 113, ---Train loss:  0.3835482 ---Val loss:  0.4293292 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 114, ---Train loss:  0.3835218 ---Val loss:  0.4291206 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 115, ---Train loss:  0.3841447 ---Val loss:  0.43085304 ---Train acc:  0.89 ---Val acc.:  0.84\n",
      "Epoch: 116, ---Train loss:  0.38340744 ---Val loss:  0.42687315 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 117, ---Train loss:  0.3837774 ---Val loss:  0.42893758 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 118, ---Train loss:  0.38360023 ---Val loss:  0.425666 ---Train acc:  0.885 ---Val acc.:  0.87\n",
      "Epoch: 119, ---Train loss:  0.383564 ---Val loss:  0.42985225 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 120, ---Train loss:  0.38390204 ---Val loss:  0.42608488 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 121, ---Train loss:  0.3840868 ---Val loss:  0.43018928 ---Train acc:  0.89 ---Val acc.:  0.84\n",
      "Epoch: 122, ---Train loss:  0.38392523 ---Val loss:  0.42975074 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 123, ---Train loss:  0.38394594 ---Val loss:  0.42965907 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 124, ---Train loss:  0.38345256 ---Val loss:  0.42532977 ---Train acc:  0.885 ---Val acc.:  0.87\n",
      "Epoch: 125, ---Train loss:  0.38333926 ---Val loss:  0.42771766 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 126, ---Train loss:  0.38338935 ---Val loss:  0.42965853 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 127, ---Train loss:  0.38349837 ---Val loss:  0.42703962 ---Train acc:  0.885 ---Val acc.:  0.87\n",
      "Epoch: 128, ---Train loss:  0.38335603 ---Val loss:  0.43066248 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 129, ---Train loss:  0.38342598 ---Val loss:  0.43114343 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 130, ---Train loss:  0.3832899 ---Val loss:  0.42722613 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 131, ---Train loss:  0.3839218 ---Val loss:  0.4272781 ---Train acc:  0.8825 ---Val acc.:  0.88\n",
      "Epoch: 132, ---Train loss:  0.38483986 ---Val loss:  0.434089 ---Train acc:  0.895 ---Val acc.:  0.83\n",
      "Epoch: 133, ---Train loss:  0.38473403 ---Val loss:  0.42541805 ---Train acc:  0.88 ---Val acc.:  0.87\n",
      "Epoch: 134, ---Train loss:  0.38316685 ---Val loss:  0.42696851 ---Train acc:  0.885 ---Val acc.:  0.87\n",
      "Epoch: 135, ---Train loss:  0.38358647 ---Val loss:  0.4301408 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 136, ---Train loss:  0.38317886 ---Val loss:  0.4276179 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 137, ---Train loss:  0.3832079 ---Val loss:  0.4263307 ---Train acc:  0.885 ---Val acc.:  0.87\n",
      "Epoch: 138, ---Train loss:  0.38397646 ---Val loss:  0.4257832 ---Train acc:  0.89 ---Val acc.:  0.85999995\n",
      "Epoch: 139, ---Train loss:  0.38329682 ---Val loss:  0.43076453 ---Train acc:  0.8825 ---Val acc.:  0.84999996\n",
      "Epoch: 140, ---Train loss:  0.3831685 ---Val loss:  0.42646685 ---Train acc:  0.885 ---Val acc.:  0.87\n",
      "Epoch: 141, ---Train loss:  0.3836214 ---Val loss:  0.43203196 ---Train acc:  0.89 ---Val acc.:  0.84\n",
      "Epoch: 142, ---Train loss:  0.3833295 ---Val loss:  0.42414555 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 143, ---Train loss:  0.38371855 ---Val loss:  0.4335794 ---Train acc:  0.8875 ---Val acc.:  0.84\n",
      "Epoch: 144, ---Train loss:  0.38346153 ---Val loss:  0.4239994 ---Train acc:  0.88 ---Val acc.:  0.87\n",
      "Epoch: 145, ---Train loss:  0.38355637 ---Val loss:  0.43063194 ---Train acc:  0.89 ---Val acc.:  0.84999996\n",
      "Epoch: 146, ---Train loss:  0.38376656 ---Val loss:  0.4238863 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 147, ---Train loss:  0.38307163 ---Val loss:  0.42759693 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 148, ---Train loss:  0.38321462 ---Val loss:  0.4302304 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 149, ---Train loss:  0.38295868 ---Val loss:  0.42662552 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 150, ---Train loss:  0.38331226 ---Val loss:  0.42897478 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 151, ---Train loss:  0.38295385 ---Val loss:  0.42623192 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 152, ---Train loss:  0.38298637 ---Val loss:  0.42633718 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 153, ---Train loss:  0.38315752 ---Val loss:  0.42812648 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 154, ---Train loss:  0.3832917 ---Val loss:  0.42611912 ---Train acc:  0.885 ---Val acc.:  0.87\n",
      "Epoch: 155, ---Train loss:  0.38305527 ---Val loss:  0.42996883 ---Train acc:  0.885 ---Val acc.:  0.84999996\n",
      "Epoch: 156, ---Train loss:  0.3828899 ---Val loss:  0.42712122 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 157, ---Train loss:  0.382888 ---Val loss:  0.42761225 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 158, ---Train loss:  0.3832228 ---Val loss:  0.4318118 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 159, ---Train loss:  0.38297805 ---Val loss:  0.42530555 ---Train acc:  0.885 ---Val acc.:  0.87\n",
      "Epoch: 160, ---Train loss:  0.3829271 ---Val loss:  0.42766583 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 161, ---Train loss:  0.38288078 ---Val loss:  0.42758974 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 162, ---Train loss:  0.38323095 ---Val loss:  0.4314559 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 163, ---Train loss:  0.38296998 ---Val loss:  0.42532498 ---Train acc:  0.885 ---Val acc.:  0.87\n",
      "Epoch: 164, ---Train loss:  0.38330626 ---Val loss:  0.43063128 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 165, ---Train loss:  0.38296652 ---Val loss:  0.42416212 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 166, ---Train loss:  0.38376254 ---Val loss:  0.43312293 ---Train acc:  0.8925 ---Val acc.:  0.83\n",
      "Epoch: 167, ---Train loss:  0.38290164 ---Val loss:  0.42786455 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 168, ---Train loss:  0.38490573 ---Val loss:  0.42062294 ---Train acc:  0.88 ---Val acc.:  0.87\n",
      "Epoch: 169, ---Train loss:  0.38705483 ---Val loss:  0.43969488 ---Train acc:  0.8925 ---Val acc.:  0.83\n",
      "Epoch: 170, ---Train loss:  0.3840871 ---Val loss:  0.424702 ---Train acc:  0.88 ---Val acc.:  0.88\n",
      "Epoch: 171, ---Train loss:  0.38338768 ---Val loss:  0.43200263 ---Train acc:  0.89 ---Val acc.:  0.84\n",
      "Epoch: 172, ---Train loss:  0.38356125 ---Val loss:  0.42361286 ---Train acc:  0.88 ---Val acc.:  0.88\n",
      "Epoch: 173, ---Train loss:  0.38280913 ---Val loss:  0.42506278 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 174, ---Train loss:  0.38344786 ---Val loss:  0.43113625 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 175, ---Train loss:  0.3831003 ---Val loss:  0.42847574 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 176, ---Train loss:  0.38351458 ---Val loss:  0.42456558 ---Train acc:  0.8825 ---Val acc.:  0.88\n",
      "Epoch: 177, ---Train loss:  0.3832619 ---Val loss:  0.42376566 ---Train acc:  0.88 ---Val acc.:  0.88\n",
      "Epoch: 178, ---Train loss:  0.38281834 ---Val loss:  0.4257856 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 179, ---Train loss:  0.38330513 ---Val loss:  0.42321905 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 180, ---Train loss:  0.3827093 ---Val loss:  0.42775667 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 181, ---Train loss:  0.38316917 ---Val loss:  0.4296865 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 182, ---Train loss:  0.38269404 ---Val loss:  0.42565843 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 183, ---Train loss:  0.38310298 ---Val loss:  0.42769194 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 184, ---Train loss:  0.38324836 ---Val loss:  0.4311135 ---Train acc:  0.89 ---Val acc.:  0.84\n",
      "Epoch: 185, ---Train loss:  0.38328728 ---Val loss:  0.42306572 ---Train acc:  0.88 ---Val acc.:  0.88\n",
      "Epoch: 186, ---Train loss:  0.38273123 ---Val loss:  0.42470145 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 187, ---Train loss:  0.38286155 ---Val loss:  0.43017435 ---Train acc:  0.8875 ---Val acc.:  0.84999996\n",
      "Epoch: 188, ---Train loss:  0.38294166 ---Val loss:  0.42784506 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 189, ---Train loss:  0.38273308 ---Val loss:  0.427323 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 190, ---Train loss:  0.38269275 ---Val loss:  0.42615446 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 191, ---Train loss:  0.3830362 ---Val loss:  0.4259626 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 192, ---Train loss:  0.38424164 ---Val loss:  0.4221842 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 193, ---Train loss:  0.38279113 ---Val loss:  0.4275159 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 194, ---Train loss:  0.38319054 ---Val loss:  0.42912108 ---Train acc:  0.8825 ---Val acc.:  0.85999995\n",
      "Epoch: 195, ---Train loss:  0.38325372 ---Val loss:  0.42487803 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 196, ---Train loss:  0.38281974 ---Val loss:  0.4291553 ---Train acc:  0.885 ---Val acc.:  0.85999995\n",
      "Epoch: 197, ---Train loss:  0.38304132 ---Val loss:  0.429425 ---Train acc:  0.885 ---Val acc.:  0.84999996\n",
      "Epoch: 198, ---Train loss:  0.3827973 ---Val loss:  0.42643645 ---Train acc:  0.8875 ---Val acc.:  0.85999995\n",
      "Epoch: 199, ---Train loss:  0.3830133 ---Val loss:  0.42427814 ---Train acc:  0.8825 ---Val acc.:  0.87\n",
      "Epoch: 200, ---Train loss:  0.3826642 ---Val loss:  0.42809522 ---Train acc:  0.89 ---Val acc.:  0.85999995\n"
     ]
    }
   ],
   "source": [
    "key = jax.random.split(key)[0]\n",
    "params = jnp.copy(initial_params)\n",
    "opt_state = optimizer.init(initial_params)\n",
    "\n",
    "train_cost_epochs, train_acc_epochs, val_cost_epochs, val_acc_epochs = [], [], [], []\n",
    "for epoch in range(1,n_epochs+1):\n",
    "    idxs_dataset = jax.random.choice(key, jnp.array(list(range(X_train.shape[0]))), shape=(X_train.shape[0],), replace=False)\n",
    "    key = jax.random.split(key)[0]\n",
    "    for i in gen_batches(X_train.shape[0], batch_size): ## we use gen_batches to optimize and calculate the gradient during each batch\n",
    "        idxs = idxs_dataset[i]\n",
    "        params, opt_state, cost = optimizer_update(opt_state, params, X_train[idxs, :], y_train[idxs])\n",
    "        cost = binary_crossentropy(X_train, y_train, params,)\n",
    "        val_cost = binary_crossentropy(X_val, y_val, params,)\n",
    "        train_acc = accuracy(X_train, y_train, params,) \n",
    "        val_acc = accuracy(X_val, y_val, params,)\n",
    "    train_cost_epochs.append(cost)\n",
    "    val_cost_epochs.append(val_cost)\n",
    "    # Accuracy during training and validation\n",
    "    train_acc_epochs.append(train_acc)\n",
    "    val_acc_epochs.append(val_acc)\n",
    "    print(f\"Epoch: {epoch}, ---Train loss: \", cost, \"---Val loss: \", val_cost, \"---Train acc: \", train_acc,\n",
    "          \"---Val acc.: \", val_acc)\n",
    "    optimal_params = params  # Optimal parameters after training saved here, ready to be used for predictions."
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-16T14:13:26.366851Z",
     "start_time": "2024-03-16T14:08:53.040563Z"
    }
   },
   "id": "6699730c5d162c7b",
   "execution_count": 22
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Plots"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a0ebfe8c9b2bdb38"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"default\")\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "axs[0].plot(train_acc_epochs, label=\"Train acc\", color=\"orange\")\n",
    "axs[0].plot(val_acc_epochs, label=\"Val acc\", color=\"orangered\", alpha=0.5, ls='--')\n",
    "axs[0].set_xlabel(\"Epoch\")\n",
    "axs[1].plot(train_cost_epochs, label=\"Train loss\", color=\"royalblue\", mfc='white', markersize=6, lw=2)\n",
    "axs[1].plot(val_cost_epochs, label=\"Val loss\", color=\"darkblue\", alpha=0.5, mfc='white', markersize=6, ls='--')\n",
    "axs[1].set_xlabel(\"Epoch\")\n",
    "axs[1].legend(fontsize=20, framealpha=0, ncol=2, columnspacing=0.4, loc='upper left', bbox_to_anchor=(-0.1, 1.35))\n",
    "axs[0].legend(fontsize=20, framealpha=0, ncol=2, columnspacing=0.4, loc='upper left', bbox_to_anchor=(-0.1, 1.35))\n",
    "axs[0].set_xlabel(r'$Epoch$', fontsize=15)\n",
    "axs[1].set_xlabel(r'$Epoch$', fontsize=15)\n",
    "axs[0].tick_params(direction='in', length=8, width=2, top='on', right='on', labelsize=10)\n",
    "axs[1].tick_params(direction='in', length=8, width=2, top='on', right='on', labelsize=10)\n",
    "plt.tight_layout()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-16T14:19:40.973217Z",
     "start_time": "2024-03-16T14:19:39.306442Z"
    }
   },
   "id": "b4da3727b17358b1",
   "execution_count": 31
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Test the model\n",
    "\n",
    "We now create the TEST set by using the SECOND tuple of Keras: in this way we are 100% sure that the model will see new unseen images!"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "dd70807415454e36"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "classes_of_items = [0,1]\n",
    "num_samples = 300\n",
    "num_samples_per_class = num_samples / 2\n",
    "\n",
    "(_, _), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "mask = np.isin(y_test, classes_of_items)\n",
    "X_test_filtered = X_test[mask]\n",
    "y_test_filtered = y_test[mask]\n",
    "\n",
    "X_test_new = [down_sample(x_test, 8) for x_test in X_test_filtered]\n",
    "X_test_final = np.array(X_test_new).reshape(len(X_test_new), -1) / 255.0\n",
    "y_test_final = np.array(y_test_filtered)\n",
    "\n",
    "selected_indices = []\n",
    "for class_idx in classes_of_items:\n",
    "    class_indices = np.where(y_test_filtered == class_idx)[0][:int(num_samples_per_class)]\n",
    "    selected_indices.extend(class_indices)\n",
    "\n",
    "# Shuffle selected indices\n",
    "np.random.shuffle(selected_indices)\n",
    "\n",
    "\n",
    "X_test_final = X_test_final[selected_indices]\n",
    "y_test_final = y_test_final[selected_indices]\n",
    "X_test_final = jnp.asarray(X_test_final)\n",
    "y_test_final = jnp.asarray(y_test_final)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-16T14:16:20.362216Z",
     "start_time": "2024-03-16T14:16:19.667999Z"
    }
   },
   "id": "b59bae9561908448",
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of 0s in the TEST set: 150\n",
      " \n",
      "Number of 1s in the TEST set: 150\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of 0s in the TEST set:\", np.count_nonzero(y_test_final == 0))\n",
    "print(\" \")\n",
    "print(\"Number of 1s in the TEST set:\", np.count_nonzero(y_test_final == 1))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-16T14:17:38.814716Z",
     "start_time": "2024-03-16T14:17:38.691151Z"
    }
   },
   "id": "883a61e9c967380f",
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.87"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def prediction(ytrue, qnn, opt_weights):\n",
    "    ypred = jnp.array(qnn(X_test_final.T, opt_weights))  ## I used .T because I had a bug without it.\n",
    "    estimated_lab = jnp.array([jnp.argmax(label) for label in ypred])\n",
    "    return [np.round(accuracy_score(jnp.array(ytrue), estimated_lab),2), estimated_lab]\n",
    "\n",
    "prediction(y_test_final, quantum_nn, optimal_params)[0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-16T14:18:39.489066Z",
     "start_time": "2024-03-16T14:18:36.910662Z"
    }
   },
   "id": "99dc700beb3ad8c",
   "execution_count": 28
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score:  0.8773006134969326\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay(confusion_matrix(y_true=y_test_final, y_pred=prediction(y_test_final, quantum_nn, optimal_params)[1])).plot()\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "print(\"F1-score: \", f1_score(y_test_final, prediction(y_test_final, quantum_nn, optimal_params)[1]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-16T14:21:30.688422Z",
     "start_time": "2024-03-16T14:21:27.918307Z"
    }
   },
   "id": "18c9d3f7bb2d4b2c",
   "execution_count": 33
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x1400 with 100 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_mnist_images(images, estimated_labels, num_images=100, num_cols=10):\n",
    "    \"\"\"\n",
    "    Plot a grid of MNIST images.\n",
    "    \n",
    "    Args:\n",
    "    - images (numpy.ndarray): Array of MNIST images\n",
    "    - estimated_labels (numpy.ndarray): Array of estimated labels for the images\n",
    "    - num_images (int): Number of images to plot (default is 100)\n",
    "    - num_cols (int): Number of columns in the grid (default is 10)\n",
    "    \"\"\"\n",
    "    num_rows = num_images // num_cols\n",
    "    fig, axes = plt.subplots(num_rows, num_cols, figsize=(14, 14))\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        ax.imshow(images[i].reshape((8, 8)), cmap='viridis')  # Corrected image shape\n",
    "        label_color = 'green' if estimated_labels[i] == y_test_final[i] else 'red'\n",
    "        ax.set_xlabel(\"Pred: \" + str(estimated_labels[i]), color=label_color)  # Setting label color based on match\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "plot_mnist_images(X_test_final[:100], prediction(y_test_final, quantum_nn, optimal_params)[1][:100])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-16T14:24:16.320434Z",
     "start_time": "2024-03-16T14:24:09.153914Z"
    }
   },
   "id": "49f6c068d2a45b26",
   "execution_count": 36
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Thanks for reading and watching :)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7156c22bde69395a"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}