Call the vgg.py class and train on the given images

model.py

import numpy as np
import tensorflow as tf
from scipy.misc import imread, imresize
from vgg16 import vgg16
import math
import matplotlib.pyplot as plt
from utils import *
import h5py

def model(X_train, Y_train, learning_rate=0.009, X=None, Y=None, weights=None, sess=None, num_epochs=10, minibatch_size=5, print_cost=True):
    m = X_train.shape[0]
    costs = []
    if weights is not None and sess is not None:
        vgg = vgg16(X, weights, sess)
    fc4l = vgg.fc4l
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=fc4l, labels=Y))
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
    init = tf.global_variables_initializer()
    sess.run(init)
    for epoch in range(num_epochs):
        minibatch_cost = 0.
        num_minibatches = int(m / minibatch_size)
        minibatches = random_mini_batches(X_train, Y_train, minibatch_size)
        for (minibatch_X, minibatch_Y) in minibatches:
            _, temp_cost = sess.run([optimizer, cost], feed_dict={Y:minibatch_Y, vgg.imgs:minibatch_X})
            minibatch_cost += temp_cost / num_minibatches
        # Print the cost every epoch
        if print_cost == True and epoch % 1 == 0:
            print ("Cost after epoch %i: %f" % (epoch, minibatch_cost))
        if print_cost == True and epoch % 1 == 0:
            costs.append(minibatch_cost)
    params = {}
    keys = []
    for key in sorted(np.load(weights)):
        keys.append(key)
    index = -1

    for item in vgg.parameters:
        index += 1
        params[keys[index]] = sess.run(item)
    np.savez("output_weight", **params)
    # plot the cost
    plt.plot(np.squeeze(costs))
    plt.ylabel('cost')
    plt.xlabel('iterations (per tens)')
    plt.title("Learning rate =" + str(learning_rate))
    plt.show()

sess = tf.Session()
file1 = h5py.File('faceData_v2.h5','r')
file2 = h5py.File('nonface_small.h5','r')
Y = np.array([[1,0]])
X_train1 = np.array(file1["X_train"])
X_train2 = np.array(file2["X_train_30000"])
count = -1
fac = np.array([[1,0]])
non_fac = np.array([[0,1]])
for data in X_train1:
    count += 1
    if count == 10:
        break
    Y = np.concatenate((Y,fac),axis=0)
for data in X_train2:
    if count == 20:
        break
    count += 1
    Y = np.concatenate((Y,non_fac),axis=0)
X = np.concatenate((X_train1[0:10,:,:,:],X_train2[0:10,:,:,:]), axis=0)
X_train1 = None
X_train2 = None
permutation = np.random.permutation(count)
X_train = X[permutation,:,:,:]
Y_train = Y[permutation,:]
X = tf.placeholder(tf.float32, [None, 224, 224, 3])
Y = tf.placeholder(tf.float32, [None, 2])
print('data loaded')
model(X_train, Y_train, 0.009, X, Y, 'init_weights.npz', sess)